---------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/danielbennett/Dropbox/Hyderabad (DB & WY)/Analysis/replication/replication.log
  log type:  text
 opened on:  15 Feb 2018, 10:10:30

. set more off

. 
. 
. *** THE MARKET FOR HIGH QUALITY MEDICINE: RETAIL CHAIN ENTRY AND DRUG QUALITY IN INDIA
. *** DANIEL BENNETT AND WESLEY YIN
. *** FEBRUARY 2018
. 
. 
. *****************
. ** INTERACTION **
. *****************
. 
. cap program drop ix

. program define ix
  1. cap gen `1'_`2'=`1'*`2'
  2. end

. 
. 
. ******************************
. ** CREATE BASELINE VARIABLE **
. ******************************
. 
. capture program drop bse

. program define bse
  1. gen tv=`1' if round==1
  2. bysort mkt_id pharm_id drg scenario: egen `1'b=max(tv)
  3. drop tv
  4. end

. 
. ****************************
. ** PROPORTIONAL SELECTION **
. ****************************
. 
. * 1=LHS VARIABLE
. * 2=CONTROLS
. * 3=SUB-MARKET VARS FOR RMAX
. * 4=ADDITIONS TO MAIN SPEC (E.G. WEIGHTS)
. 
. capture program drop psroutine

. program define psroutine
  1. quietly reg `1' mk_* post_mk_* `3'
  2. global rmaxvar=e(r2)
  3. quietly reg `1' post post_trt mk_* `2' `4'
  4. psacalc delta post_trt, mcontrol(post mk_*) rmax($rmaxvar)
  5. macro drop rmaxvar
  6. end

. 
. 
. ****************************************************
. ** PROPORTIONAL SELECTION FOR 3-ROUND REGRESSIONS **
. ****************************************************
. 
. * 1=LHS VARIABLE
. * 2=CONTROLS
. * 3=WEIGHTS
. 
. capture program drop psroutine_3round

. program define psroutine_3round
  1. quietly reg `1' r2_mk* r3_mk* mk_* `3'
  2. global rmaxvar=e(r2)
  3. quietly reg `1' r2_trt r3_trt r2 r3 mk_* `2' `3'
  4. psacalc delta r2_trt, mcontrol(r3_trt r2 r3 mk_*) rmax($rmaxvar)
  5. psacalc delta r3_trt, mcontrol(r2_trt r2 r3 mk_*) rmax($rmaxvar)
  6. macro drop rmaxvar
  7. end

. 
. 
. ************
. ** MACROS **
. ************
. 
. global urc      "tag_unitround==1  & medplus~=1"                        // UNIT-ROUND TAG

. global prc      "tag_pharmround==1 & medplus~=1"                        // PHARMACY-ROUND TAG

. global demo_m   "inc_m edu_m deg_m cst_m tfw_m"                                     // MARKET DEMOGRAPHICS

. global hlth_m   "fevr_m diar_m cold_m injy_m"                                   // MARKET HEALTH STATUS

. global demo_bix "inc_mb edu_mb deg_mb cst_mb tfw_mb post_inc_mb post_edu_mb post_deg_mb post_cst_mb post_tfw_mb"                             // BASELINE MARK
> ET DEMOGRAPHICS X POST

. global hlth_bix "fevr_mb diar_mb cold_mb injy_mb post_fevr_mb post_diar_mb post_cold_mb post_injy_mb"                                           // BASELINE M
> ARKET HEALTH STATUS X POST

. global supp_bix "retpctb signsb phageb tr_allb post_retpctb post_signsb post_phageb post_tr_allb"                                                            
>    // BASELINE PHARMACY CHARACTERISTICS X POST

. 
. 
. 
. 
. *****************
. ** IMPORT DATA **
. *****************
. 
. import delimited traffic, clear
(76 vars, 298 obs)

. save traffic, replace
file traffic.dta saved

. 
. import delimited census, clear
(75 vars, 1,053 obs)

. save census, replace
file census.dta saved

. 
. import delimited consumer, clear
(95 vars, 5,234 obs)

. save consumer, replace
file consumer.dta saved

. 
. import delimited final, clear
(196 vars, 1,717 obs)

. save final, replace
file final.dta saved

. 
. import delimited hyd_weather, clear
(5 vars, 18,052 obs)

. save hyd_weather, replace
file hyd_weather.dta saved

. 
. 
. 
. 
. 
. /*
> ************************************************************************************************************************
> ** FIGURE 1A: THE PRICE DISTRIBUTIONS FOR INDIAN PHARMACOPEIA COMPLIANT (I.E. HIGH QUALITY) AND NON-COMPLIANT SAMPLES **
> ************************************************************************************************************************
> 
> use final, clear
> twoway kdensity pricepertab_usd if pass==1, bwidth(.01) scheme(s1mono) legend(label(1 "IP Compliant Samples")) || kdensity  pricepertab_usd if pass==0, bwidt
> h(.01) lwidth(thick) legend(label(2 "IP Non-Compliant Samples")) ytitle(Kernel Density) xtitle(Price per 500mg Tablet (USD))
> 
> XXX
> */
. 
. 
. 
. 
. /*
> *******************************************************
> ** FIGURE 1B: ACTUAL AND PERCEIVED QUALITY BY MARKET ** 
> *******************************************************
> 
> use final, clear
> bysort mkt_id round: egen passm=mean(pass)
> bysort mkt_id round: keep if _n==1
> 
> twoway (scatter qualm passm, msymbol(+) scheme(s1mono) xtitle(Marketwide Indian Pharmacopeia Compliance) ytitle(Perceived Quality Among Consumers) legend(off
> )) (lfit qualm passm)
> 
> XXX
> */
. 
. 
. 
. 
. 
. /*
> *****************************************************************************************************
> ** FIGURE 2: BASELINE DISTRIBUTIONS OF NON-BINARY CHARACTERISTICS IN TREATMENT AND CONTROL MARKETS **
> *****************************************************************************************************
> 
> use final, clear
> 
> local var pricepertab_usd
> twoway kdensity `var' if trt==0 & round==1 & tag_unitround==1, scheme(s1mono) ylabel(0[15]15) lcolor(gs10) lwidth(thick) bwidth(0.012) xtitle(US Dollars) leg
> end(label(1 "Control Markets")) || kdensity `var' if trt==1 & round==1 & tag_unitround==1, lcolor(black) lwidth(thick) lpattern(solid) bwidth(0.012) ytitle(K
> ernel Density) title(Price per Tablet) legend(label(2 "Treatment Markets")) legend(off)
> graph save dens_prc.gph, replace
> ksmirnov `var' if round==1 & tag_unitround==1, by(trt) exact
> 
> local var l_api_pct
> twoway kdensity `var' if trt==0 & round==1 & tag_unitround==1, scheme(s1mono) bwidth(0.006) ylabel(0[30]30) lcolor(gs10) lwidth(thick) xtitle(Absolute Percen
> t Deviation) legend(label(1 "Control Markets")) || kdensity `var' if trt==1 & round==1 & tag_unitround==1, lcolor(black) lwidth(thick) ytitle(Kernel Density)
>  legend(label(2 "Treatment Markets")) legend(off) bwidth(0.006) title(Active Ingredient)
> graph save dens_api.gph, replace
> ksmirnov `var' if round==1 & tag_unitround==1, by(trt) exact
> 
> local var l_uniform_absmax
> twoway kdensity `var' if trt==0 & round==1 & tag_unitround==1, scheme(s1mono) ylabel(0[0.8]0.8) lcolor(gs10) lwidth(thick) xtitle(Uniformity) bwidth(0.2)|| k
> density `var' if trt==1 & round==1 & tag_unitround==1, lcolor(black) lwidth(thick) ytitle(Kernel Density) legend(off) bwidth(0.2) title(Uniformity) 
> graph save dens_uni.gph, replace
> ksmirnov `var' if round==1 & tag_unitround==1, by(trt) exact
> 
> local var l_dissol_min
> twoway kdensity `var' if trt==0 & round==1 & tag_unitround==1, scheme(s1mono) ylabel(0[0.15]0.15) lcolor(gs10) lwidth(thick) xtitle(Dissolution) bwidth(1) ||
>  kdensity `var' if trt==1 & round==1 & tag_unitround==1, lcolor(black) lwidth(thick) ytitle(Kernel Density) legend(off) title(Dissolution) bwidth(1) 
> graph save dens_dis.gph, replace
> ksmirnov `var' if round==1 & tag_unitround==1, by(trt) exact
> 
> local var tte
> twoway kdensity `var' if trt==0 & round==1 & tag_unitround==1, scheme(s1mono) lcolor(gs10) lwidth(thick) xtitle(Days) bwidth(80) ylabel(0[0.002].002) || kden
> sity `var' if trt==1 & round==1 & tag_unitround==1, lcolor(black) lwidth(thick) ytitle(Kernel Density) legend(off) title(Days Until Expiry) bwidth(80)
> graph save dens_tte.gph, replace
> ksmirnov `var' if round==1 & tag_unitround==1, by(trt) exact
> 
> local var tr_all
> twoway kdensity `var' if trt==0 & round==1 & tag_pharmround==1, scheme(s1mono) lcolor(gs10) lwidth(thick) xtitle(Number of Customers) bwidth(12) ylabel(0[0.0
> 2]0.02) || kdensity `var' if trt==1 & round==1 & tag_pharmround==1, lcolor(black) lwidth(thick) ytitle(Kernel Density) legend(off) title(Customer Traffic) bw
> idth(12)
> graph save dens_trf.gph, replace
> ksmirnov `var' if round==1 & tag_pharmround==1, by(trt) exact
> 
> local var distw
> twoway kdensity `var' if trt==0 & round==1 & tag_pharmround==1, scheme(s1mono) lcolor(gs10) lwidth(thick) xtitle(Kilometers) bwidth(.2) ylabel(0[1.2]1.2) || 
> kdensity `var' if trt==1 & round==1 & tag_pharmround==1, lcolor(black) lwidth(thick) ytitle(Kernel Density) legend(off) title(Distance to Market Center) bwid
> th(.2)
> graph save dens_dmc.gph, replace
> ksmirnov `var' if round==1 & tag_pharmround==1, by(trt) exact
> 
> 
> use consumer, clear
> local var ln_incr_usd
> twoway kdensity `var' if trt==0 & round==1 & int_loc==2, bwidth(0.25) ylabel(0[1]1) scheme(s1mono) lcolor(gs10) lwidth(thick) xtitle(US Dollars) || kdensity 
> `var' if trt==1 & round==1 & int_loc==2, bwidth(0.25) lcolor(black) lwidth(thick) ytitle(Kernel Density) title(Log Monthly Income) legend(off)
> graph save dens_inc.gph, replace
> ksmirnov `var' if round==1 & int_loc==2, by(trt) exact
> 
> local var edu_years
> twoway hist `var' if trt==1 & round==1 & int_loc==2, scheme(s1mono) color(black) ylabel(0[0.15]0.15) start(0) width(2) || hist `var' if trt==0 & round==1 & i
> nt_loc==2, fcolor(none) lcolor(gs10) lwidth(thick) start(0) width(2) legend(off) xtitle(Years) ytitle(Frequency) title(Education)
> graph save dens_edu.gph, replace
> ksmirnov `var' if round==1 & int_loc==2, by(trt) exact
> 
> local var hsize
> twoway hist `var' if trt==1 & round==1 & int_loc==2, scheme(s1mono) ylabel(0[0.4]0.4) color(black) start(2) width(1) || hist `var' if trt==0 & round==1 & int
> _loc==2, fcolor(none) lcolor(gs10) lwidth(thick) start(2) width(1) legend(off) xtitle(Household Size) ytitle(Frequency) title(Household Size)
> graph save dens_hhs.gph, replace
> ksmirnov `var' if round==1 & int_loc==2, by(trt) exact
> 
> grc1leg dens_prc.gph dens_api.gph dens_uni.gph dens_dis.gph dens_tte.gph dens_trf.gph dens_dmc.gph dens_inc.gph dens_edu.gph dens_hhs.gph, cols(2) rows(5)   
>    legendfrom(dens_prc.gph) scheme(s1mono) 
> */
. 
. 
. 
. 
. 
. 
. /*
> *****************************************
> ** FIGURE 3: QUALITY AND PRICE CHANGES **
> *****************************************
> 
> use if tag_unitround==1 using final, clear
> 
> xtset unit round
> set scheme s1mono
> 
> label var pass "Percent Compliance with Indian Pharmacopeia"
> label var pricepertab_usd "Price per Tablet (2010 USD)"
> label var round "Survey Round"
> 
> xtgraph pass if medplus==0, gr(trt) label 
> graph save qualall.gph, replace
> 
> xtgraph pricepertab_usd if medplus==0, gr(trt) 
> graph save priceall.gph, replace
> 
> graph combine qualall.gph priceall.gph, cols(1) rows(2) title("") scheme(s1mono) ysize(11) xsize(7)
> 
> XXX
> */
. 
. 
. 
. 
. 
. /*
> **************************************************************************************
> ** FIGURE 4A: THE DENSITY OF ACTIVE INGREDIENT CONCENTRATION FOR NON-NATIONAL DRUGS **
> **************************************************************************************
> 
> use final, clear
> 
> ** CONTROL MARKETS
> twoway kdensity l_api_pct_cont if round==1 & tag_unitround==1 & trt==0 & nn==1, title(Control Markets) lcolor(gs11) xlabel(80[5]115) ylabel(0[0.05].2) bwidth
> (1.5) scheme(s1mono) xtitle(% of Correct Level) ytitle(Kernel Density) legend(label(1 "Round 1")) lwidth(thick) || kdensity l_api_pct_cont if round==2 & tag_
> unitround==1 & trt==0 & nn==1, lcolor(black) bwidth(1.5) legend(label(2 "Round 2")) lwidth(thick)
> graph save api_ctl.gph, replace
> ksmirnov l_api_pct_cont, by(round), if tag_unitround==1 & trt==0 & nn==1
> 
> ** TREATMENT MARKETS
> twoway kdensity l_api_pct_cont if round==1 & tag_unitround==1 & trt==1 & nn==1, title(Treatment Markets) lcolor(gs11) xlabel(80[5]115) ylabel(0[0.05].2) bwid
> th(1.5) scheme(s1mono) xtitle(% of Correct Level) ytitle(Kernel Density) legend(label(1 "Round 1")) lwidth(thick) || kdensity l_api_pct_cont if round==2 & ta
> g_unitround==1 & trt==1 & nn==1, lcolor(black) bwidth(1.5) legend(label(2 "Round 2")) lwidth(thick)
> graph save api_trt.gph, replace
> ksmirnov l_api_pct_cont, by(round), if tag_unitround==1 & trt==1 & nn==1
> 
> graph combine api_ctl.gph api_trt.gph, cols(2) rows(1) title("") scheme(s1mono) ysize(4) xsize(7)
> 
> XXX
> */
. 
. 
. /*
> ******************************************************************
> ** FIGURE 4B: THE DENSITY OF DISSOLUTION FOR NON-NATIONAL DRUGS **
> ******************************************************************
> 
> use final, clear
> 
> ** CONTROL MARKETS
> twoway kdensity l_dissol_min if round==1 & tag_unitround==1 & trt==0 & nn==1, title(Control Markets) lcolor(gs11) xlabel(30[10]100) ylabel(0[0.05].15) bwidth
> (1.5) scheme(s1mono) xtitle(Dissolution) ytitle(Kernel Density) legend(label(1 "Round 1")) lwidth(thick) || kdensity l_dissol_min if round==2 & tag_unitround
> ==1 & trt==0 & nn==1, lcolor(black) bwidth(1.5) legend(label(2 "Round 2")) lwidth(thick)
> graph save dissol_ctl.gph, replace
> ksmirnov l_dissol_min, by(round), if tag_unitround==1 & trt==0 & nn==1
> 
> ** TREATMENT MARKETS
> twoway kdensity l_dissol_min if round==1 & tag_unitround==1 & trt==1 & nn==1, title(Treatment Markets) lcolor(gs11) xlabel(30[10]100) ylabel(0[0.05].15) bwid
> th(1.5) scheme(s1mono) xtitle(Dissolution) ytitle(Kernel Density) legend(label(1 "Round 1")) lwidth(thick) || kdensity l_dissol_min if round==2 & tag_unitrou
> nd==1 & trt==1 & nn==1, lcolor(black) bwidth(1.5) legend(label(2 "Round 2")) lwidth(thick)
> graph save dissol_trt, replace
> ksmirnov l_dissol_min, by(round), if tag_unitround==1 & trt==1 & nn==1
> 
> graph combine dissol_ctl.gph dissol_trt.gph, cols(2) rows(1) title("") scheme(s1color) ysize(4) xsize(7)
> XXX
> */
. 
. 
. 
. 
. *******************************************************************
. ** TABLE 1: BASELINE COMPARISON OF TREATMENT AND CONTROL MARKETS **
. *******************************************************************
. 
. local panel_a  "pricepertab_usd pass l_api_pct l_uniform_absmax l_dissol_min tte"

. local panel_b  "ph_ac ph_clean tr_all distw"

. local panel_c "ln_incr_usd edu_years hsize scst tfw"

. 
. 
. // PANEL A
. use final, clear

. 
. 
. foreach var in `panel_a' {
  2. display "`var'"
  3. ttest `var', by(trt), if round==1 & medplus==0 & tag_unitround==1 
  4. }
pricepertab_usd

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     254    .1603882    .0022633    .0360717    .1559308    .1648455
       1 |     136    .1623463    .0028117    .0327899    .1567856     .167907
---------+--------------------------------------------------------------------
combined |     390     .161071    .0017689    .0349323    .1575933    .1645487
---------+--------------------------------------------------------------------
    diff |           -.0019582    .0037152               -.0092625    .0053462
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.5271
Ho: diff = 0                                     degrees of freedom =      388

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.2992         Pr(|T| > |t|) = 0.5984          Pr(T > t) = 0.7008
pass

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     259     .969112    .0107714    .1733493    .9479009     .990323
       1 |     138    .9710145    .0143332    .1683769    .9426716    .9993574
---------+--------------------------------------------------------------------
combined |     397    .9697733    .0086037    .1714266    .9528588    .9866878
---------+--------------------------------------------------------------------
    diff |           -.0019025    .0180895               -.0374663    .0336612
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.1052
Ho: diff = 0                                     degrees of freedom =      395

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.4581         Pr(|T| > |t|) = 0.9163          Pr(T > t) = 0.5419
l_api_pct

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     259    2.483351    .1567456    2.522582    2.174688    2.792015
       1 |     138    2.349986    .1889085    2.219172    1.976432    2.723539
---------+--------------------------------------------------------------------
combined |     397    2.436992     .121428    2.419435    2.198268    2.675716
---------+--------------------------------------------------------------------
    diff |            .1333658    .2552224               -.3683983      .63513
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.5225
Ho: diff = 0                                     degrees of freedom =      395

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6992         Pr(|T| > |t|) = 0.6016          Pr(T > t) = 0.3008
l_uniform_absmax

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     248    3.264677    .0622257     .979931    3.142117    3.387238
       1 |     129    3.086868    .0850044    .9654644    2.918672    3.255064
---------+--------------------------------------------------------------------
combined |     377    3.203836    .0503375    .9773769    3.104857    3.302814
---------+--------------------------------------------------------------------
    diff |            .1778092     .105843                -.030311    .3859294
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.6799
Ho: diff = 0                                     degrees of freedom =      375

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9531         Pr(|T| > |t|) = 0.0938          Pr(T > t) = 0.0469
l_dissol_min

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     247    89.62433    .2207198    3.468884    89.18959    90.05907
       1 |     132    89.61242    .3486071    4.005191     88.9228    90.30205
---------+--------------------------------------------------------------------
combined |     379    89.62018    .1879658    3.659304     89.2506    89.98977
---------+--------------------------------------------------------------------
    diff |            .0119075    .3950548               -.7648795    .7886945
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.0301
Ho: diff = 0                                     degrees of freedom =      377

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.5120         Pr(|T| > |t|) = 0.9760          Pr(T > t) = 0.4880
tte

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     248    614.6109    15.27983    240.6271    584.5155    644.7063
       1 |     134    642.2687    19.24395    222.7648    604.2049    680.3324
---------+--------------------------------------------------------------------
combined |     382    624.3128    12.00291    234.5947    600.7126    647.9131
---------+--------------------------------------------------------------------
    diff |           -27.65777    25.14502               -77.09857    21.78303
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.0999
Ho: diff = 0                                     degrees of freedom =      380

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1360         Pr(|T| > |t|) = 0.2721          Pr(T > t) = 0.8640

. 
. // PANEL B
. foreach var in `panel_b' {
  2. display "`var'"
  3. ttest `var', by(trt), if tag_pharmround==1 & round==1 & medplus==0
  4. }
ph_ac

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      64      .15625    .0457453    .3659625    .0648353    .2476647
       1 |      35    .0857143    .0480096    .2840286    -.011853    .1832815
---------+--------------------------------------------------------------------
combined |      99    .1313131    .0341171    .3394613    .0636088    .1990175
---------+--------------------------------------------------------------------
    diff |            .0705357    .0713733               -.0711205    .2121919
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.9883
Ho: diff = 0                                     degrees of freedom =       97

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.8373         Pr(|T| > |t|) = 0.3255          Pr(T > t) = 0.1627
ph_clean

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      64     4.03125    .0496453    .3971626    3.932042    4.130458
       1 |      35    3.971429    .0646349    .3823853    3.840075    4.102783
---------+--------------------------------------------------------------------
combined |      99    4.010101    .0393069    .3910986    3.932098    4.088104
---------+--------------------------------------------------------------------
    diff |            .0598214    .0824197               -.1037588    .2234017
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.7258
Ho: diff = 0                                     degrees of freedom =       97

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.7652         Pr(|T| > |t|) = 0.4697          Pr(T > t) = 0.2348
tr_all

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      65    71.46154    3.710372    29.91398    64.04922    78.87386
       1 |      35    69.45714     3.23124    19.11627    62.89047    76.02381
---------+--------------------------------------------------------------------
combined |     100       70.76    2.655017    26.55017    65.49187    76.02813
---------+--------------------------------------------------------------------
    diff |            2.004396    5.591091               -9.090943    13.09973
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.3585
Ho: diff = 0                                     degrees of freedom =       98

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6396         Pr(|T| > |t|) = 0.7207          Pr(T > t) = 0.3604
distw

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      65    .5064461    .0643547     .518844    .3778829    .6350094
       1 |      35    .5631429    .0955522    .5652942    .3689575    .7573282
---------+--------------------------------------------------------------------
combined |     100      .52629    .0533398     .533398    .4204523    .6321277
---------+--------------------------------------------------------------------
    diff |           -.0566967    .1122537               -.2794606    .1660671
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -0.5051
Ho: diff = 0                                     degrees of freedom =       98

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.3073         Pr(|T| > |t|) = 0.6146          Pr(T > t) = 0.6927

. 
. // NUMBER OF FIRMS
. ttest ce_mfirms if round==1 & tag_mktround==1, by(trt)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |      13    19.23077    1.958394    7.061089     14.9638    23.49774
       1 |       7    14.42857    2.776235    7.345228    7.635368    21.22177
---------+--------------------------------------------------------------------
combined |      20       17.55    1.643928    7.351871    14.10922    20.99078
---------+--------------------------------------------------------------------
    diff |            4.802198    3.355279               -2.246983    11.85138
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.4312
Ho: diff = 0                                     degrees of freedom =       18

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9153         Pr(|T| > |t|) = 0.1695          Pr(T > t) = 0.0847

. 
. 
. // PANEL C
. use consumer, clear

. foreach var in `panel_c'  {
  2. display "`var'"
  3. ttest `var', by(trt), if mp==0 & round==1 & int_loc==2
  4. }
ln_incr_usd

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     285    5.302137    .0267493    .4515807    5.249485    5.354789
       1 |     155    5.369025    .0595517    .7414128    5.251381    5.486668
---------+--------------------------------------------------------------------
combined |     440      5.3257    .0272104    .5707698    5.272221    5.379178
---------+--------------------------------------------------------------------
    diff |           -.0668881    .0569392               -.1787961    .0450198
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -1.1747
Ho: diff = 0                                     degrees of freedom =      438

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1204         Pr(|T| > |t|) = 0.2407          Pr(T > t) = 0.8796
edu_years

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     317    12.17035    .2656814    4.730323    11.64762    12.69308
       1 |     177    11.97175    .3852231     5.12506     11.2115      12.732
---------+--------------------------------------------------------------------
combined |     494    12.09919    .2191651    4.871189    11.66858     12.5298
---------+--------------------------------------------------------------------
    diff |            .1985956    .4574461               -.7001932    1.097384
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.4341
Ho: diff = 0                                     degrees of freedom =      492

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6678         Pr(|T| > |t|) = 0.6644          Pr(T > t) = 0.3322
hsize

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     317     4.07571    .0604611    1.076479    3.956753    4.194667
       1 |     177    4.022599    .0866525    1.152836    3.851587    4.193611
---------+--------------------------------------------------------------------
combined |     494     4.05668    .0496522    1.103574    3.959124    4.154236
---------+--------------------------------------------------------------------
    diff |            .0531109    .1036272               -.1504955    .2567174
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   0.5125
Ho: diff = 0                                     degrees of freedom =      492

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.6957         Pr(|T| > |t|) = 0.6085          Pr(T > t) = 0.3043
scst

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     298    .0637584     .014177     .244733    .0358583    .0916585
       1 |     164    .1707317    .0294721    .3774268    .1125354     .228928
---------+--------------------------------------------------------------------
combined |     462    .1017316    .0140793    .3026227    .0740641    .1293991
---------+--------------------------------------------------------------------
    diff |           -.1069733      .02903               -.1640211   -.0499255
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =  -3.6849
Ho: diff = 0                                     degrees of freedom =      460

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.0001         Pr(|T| > |t|) = 0.0003          Pr(T > t) = 0.9999
tfw

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
       0 |     317    .6435331    .0269433    .4797126    .5905221    .6965441
       1 |     177    .5706215    .0373111    .4963917    .4969868    .6442562
---------+--------------------------------------------------------------------
combined |     494    .6174089    .0218892    .4865124    .5744012    .6604166
---------+--------------------------------------------------------------------
    diff |            .0729117     .045578               -.0166399    .1624632
------------------------------------------------------------------------------
    diff = mean(0) - mean(1)                                      t =   1.5997
Ho: diff = 0                                     degrees of freedom =      492

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.9448         Pr(|T| > |t|) = 0.1103          Pr(T > t) = 0.0552

. 
. 
. 
. 
. 
. *******************************************************************************
. ** TABLE 2: TRENDS IN SOCIOECONOMIC STATUS FOR TREATMENT AND CONTROL MARKETS **
. *******************************************************************************
. 
. use if int_loc==2 using consumer, clear

. 
. foreach var in ln_incr_usd edu_years hsize scst tfw {
  2. display "`var'"
  3. reg `var' post if trt==0 & round<3, cl(mkt_id)
  4. reg `var' post if trt==1 & round<3, cl(mkt_id) 
  5. reg `var' post_trt post trt if round<3, cl(mkt_id)
  6. }
ln_incr_usd

Linear regression                               Number of obs     =        938
                                                F(1, 12)          =       0.47
                                                Prob > F          =     0.5046
                                                R-squared         =     0.0017
                                                Root MSE          =     .49956

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
 ln_incr_usd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0453303   .0658913     0.69   0.505    -.0982345    .1888952
       _cons |   5.302137   .0492061   107.75   0.000     5.194926    5.409348
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        578
                                                F(1, 6)           =       0.05
                                                Prob > F          =     0.8261
                                                R-squared         =     0.0005
                                                Root MSE          =     .67995

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
 ln_incr_usd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |    -.03384   .1474575    -0.23   0.826    -.3946556    .3269755
       _cons |   5.369025   .1187748    45.20   0.000     5.078393    5.659656
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,516
                                                F(3, 19)          =       0.21
                                                Prob > F          =     0.8890
                                                R-squared         =     0.0012
                                                Root MSE          =       .575

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
 ln_incr_usd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_trt |  -.0791704   .1544206    -0.51   0.614    -.4023765    .2440357
        post |   .0453303   .0649806     0.70   0.494    -.0906756    .1813363
         trt |   .0668881   .1228269     0.54   0.592    -.1901915    .3239678
       _cons |   5.302137    .048526   109.26   0.000      5.20057    5.403703
------------------------------------------------------------------------------
edu_years

Linear regression                               Number of obs     =        962
                                                F(1, 12)          =       0.01
                                                Prob > F          =     0.9332
                                                R-squared         =     0.0000
                                                Root MSE          =     4.5247

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
   edu_years |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0451569   .5274029     0.09   0.933    -1.103955    1.194269
       _cons |   12.17035    .407598    29.86   0.000     11.28227    13.05843
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        597
                                                F(1, 6)           =       2.49
                                                Prob > F          =     0.1653
                                                R-squared         =     0.0101
                                                Root MSE          =     5.3624

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
   edu_years |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -1.186037   .7509245    -1.58   0.165    -3.023483     .651409
       _cons |   11.97175   .5612806    21.33   0.000     10.59835    13.34516
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,559
                                                F(3, 19)          =       2.18
                                                Prob > F          =     0.1239
                                                R-squared         =     0.0158
                                                Root MSE          =     4.8623

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
   edu_years |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_trt |  -1.231194   .8828407    -1.39   0.179    -3.079001    .6166128
        post |   .0451569   .5201063     0.09   0.932    -1.043438    1.133752
         trt |  -.1985956   .6677458    -0.30   0.769    -1.596204    1.199012
       _cons |   12.17035   .4019589    30.28   0.000     11.32904    13.01166
------------------------------------------------------------------------------
hsize

Linear regression                               Number of obs     =        970
                                                F(1, 12)          =       0.92
                                                Prob > F          =     0.3551
                                                R-squared         =     0.0047
                                                Root MSE          =     1.2325

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       hsize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .1800329   .1871896     0.96   0.355    -.2278183    .5878841
       _cons |    4.07571   .1351831    30.15   0.000     3.781171    4.370248
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        601
                                                F(1, 6)           =       1.12
                                                Prob > F          =     0.3300
                                                R-squared         =     0.0045
                                                Root MSE          =     1.2037

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       hsize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .1778728   .1678427     1.06   0.330    -.2328234    .5885691
       _cons |   4.022599   .1113127    36.14   0.000     3.750226    4.294971
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,571
                                                F(3, 19)          =       0.91
                                                Prob > F          =     0.4541
                                                R-squared         =     0.0050
                                                Root MSE          =     1.2216

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       hsize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_trt |  -.0021601   .2439277    -0.01   0.993    -.5127066    .5083864
        post |   .1800329   .1845993     0.98   0.342    -.2063379    .5664037
         trt |  -.0531109   .1701598    -0.31   0.758    -.4092594    .3030376
       _cons |    4.07571   .1333124    30.57   0.000     3.796684    4.354736
------------------------------------------------------------------------------
scst

Linear regression                               Number of obs     =        864
                                                F(1, 12)          =       1.24
                                                Prob > F          =     0.2878
                                                R-squared         =     0.0026
                                                Root MSE          =     .27634

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        scst |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0298812   .0268622     1.11   0.288    -.0286466     .088409
       _cons |   .0637584   .0108672     5.87   0.000     .0400808    .0874359
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        559
                                                F(1, 6)           =       0.45
                                                Prob > F          =     0.5266
                                                R-squared         =     0.0009
                                                Root MSE          =     .36128

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        scst |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0238963   .0355583    -0.67   0.527    -.1109042    .0631117
       _cons |   .1707317   .0284169     6.01   0.001      .101198    .2402654
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,423
                                                F(3, 19)          =      12.36
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0137
                                                Root MSE          =     .31245

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        scst |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_trt |  -.0537775   .0429298    -1.25   0.226    -.1436305    .0360756
        post |   .0298812   .0264915     1.13   0.273    -.0255661    .0853285
         trt |   .1069733   .0290462     3.68   0.002      .046179    .1677677
       _cons |   .0637584   .0107172     5.95   0.000     .0413271    .0861897
------------------------------------------------------------------------------
tfw

Linear regression                               Number of obs     =        970
                                                F(1, 12)          =       0.68
                                                Prob > F          =     0.4262
                                                R-squared         =     0.0044
                                                Root MSE          =     .48995

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tfw |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0692605   .0840795    -0.82   0.426    -.2524541     .113933
       _cons |   .6435331   .0538912    11.94   0.000     .5261144    .7609519
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        601
                                                F(1, 6)           =       0.18
                                                Prob > F          =     0.6823
                                                R-squared         =     0.0009
                                                Root MSE          =     .49835

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tfw |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0328856   .0764984    -0.43   0.682    -.2200705    .1542992
       _cons |   .5706215   .0209211    27.27   0.000     .5194295    .6218135
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,571
                                                F(3, 19)          =       0.72
                                                Prob > F          =     0.5528
                                                R-squared         =     0.0054
                                                Root MSE          =     .49318

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tfw |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    post_trt |   .0363749   .1102559     0.33   0.745    -.1943933    .2671432
        post |  -.0692605    .082916    -0.84   0.414    -.2428058    .1042847
         trt |  -.0729117   .0567401    -1.29   0.214    -.1916701    .0458468
       _cons |   .6435331   .0531454    12.11   0.000     .5322985    .7547677
------------------------------------------------------------------------------

. 
. 
. 
. foreach var in ln_incr_usd edu_years hsize scst tfw {
  2. display "`var'"
  3. reg `var' post3 if trt==0 & round~=2, cl(mkt_id)
  4. reg `var' post3 if trt==1 & round~=2, cl(mkt_id) 
  5. reg `var' post3_trt post3 trt if round~=2, cl(mkt_id)
  6. }
ln_incr_usd

Linear regression                               Number of obs     =        915
                                                F(1, 12)          =       6.38
                                                Prob > F          =     0.0267
                                                R-squared         =     0.0134
                                                Root MSE          =     .60198

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
 ln_incr_usd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |   -.151608   .0600365    -2.53   0.027    -.2824162   -.0207998
       _cons |   5.302137   .0492068   107.75   0.000     5.194924    5.409349
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        575
                                                F(1, 6)           =       0.04
                                                Prob > F          =     0.8541
                                                R-squared         =     0.0002
                                                Root MSE          =      .6821

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
 ln_incr_usd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |   -.022683   .1181933    -0.19   0.854    -.3118915    .2665255
       _cons |   5.369025   .1187753    45.20   0.000     5.078392    5.659657
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,490
                                                F(3, 19)          =       4.14
                                                Prob > F          =     0.0204
                                                R-squared         =     0.0213
                                                Root MSE          =     .63408

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
 ln_incr_usd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   post3_trt |    .128925   .1269374     1.02   0.323    -.1367579     .394608
       post3 |   -.151608   .0592069    -2.56   0.019    -.2755295   -.0276865
         trt |   .0668881    .122829     0.54   0.592     -.190196    .3239722
       _cons |   5.302137   .0485269   109.26   0.000     5.200569    5.403705
------------------------------------------------------------------------------
edu_years

Linear regression                               Number of obs     =        957
                                                F(1, 12)          =      24.30
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0501
                                                Root MSE          =     4.7326

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
   edu_years |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |  -2.306285   .4678253    -4.93   0.000    -3.325588   -1.286981
       _cons |   12.17035   .4075991    29.86   0.000     11.28226    13.05843
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        598
                                                F(1, 6)           =       4.26
                                                Prob > F          =     0.0846
                                                R-squared         =     0.0182
                                                Root MSE          =     4.4611

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
   edu_years |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |  -1.328046   .6434542    -2.06   0.085    -2.902522    .2464297
       _cons |   11.97175   .5612798    21.33   0.000     10.59835    13.34515
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,555
                                                F(3, 19)          =       9.92
                                                Prob > F          =     0.0004
                                                R-squared         =     0.0406
                                                Root MSE          =     4.6302

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
   edu_years |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   post3_trt |   .9782386   .7658367     1.28   0.217    -.6246761    2.581153
       post3 |  -2.306285   .4613528    -5.00   0.000    -3.271907   -1.340662
         trt |  -.1985956   .6677474    -0.30   0.769    -1.596207    1.199016
       _cons |   12.17035   .4019599    30.28   0.000     11.32904    13.01166
------------------------------------------------------------------------------
hsize

Linear regression                               Number of obs     =        957
                                                F(1, 12)          =      29.32
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0372
                                                Root MSE          =     2.0271

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       hsize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |   .8461652   .1562614     5.42   0.000      .505701    1.186629
       _cons |    4.07571    .135184    30.15   0.000     3.781169     4.37025
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        598
                                                F(1, 6)           =      19.60
                                                Prob > F          =     0.0044
                                                R-squared         =     0.0281
                                                Root MSE          =     1.6131

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       hsize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |   .5997289   .1354747     4.43   0.004     .2682342    .9312236
       _cons |   4.022599   .1113132    36.14   0.000     3.750225    4.294972
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,555
                                                F(3, 19)          =      19.83
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0371
                                                Root MSE          =     1.8788

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       hsize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   post3_trt |  -.2464363   .2007743    -1.23   0.235    -.6666618    .1737892
       post3 |   .8461652   .1540995     5.49   0.000     .5236314    1.168699
         trt |  -.0531109   .1701614    -0.31   0.758    -.4092629    .3030411
       _cons |    4.07571   .1333137    30.57   0.000     3.796681    4.354739
------------------------------------------------------------------------------
scst

Linear regression                               Number of obs     =        903
                                                F(1, 12)          =      15.17
                                                Prob > F          =     0.0021
                                                R-squared         =     0.0268
                                                Root MSE          =      .3489

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        scst |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |   .1230185   .0315898     3.89   0.002     .0541903    .1918467
       _cons |   .0637584   .0108669     5.87   0.000     .0400814    .0874354
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        554
                                                F(1, 6)           =       0.11
                                                Prob > F          =     0.7481
                                                R-squared         =     0.0004
                                                Root MSE          =     .38673

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        scst |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |   .0164478   .0489188     0.34   0.748    -.1032521    .1361477
       _cons |   .1707317   .0284171     6.01   0.001     .1011975    .2402659
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,457
                                                F(3, 19)          =       9.56
                                                Prob > F          =     0.0005
                                                R-squared         =     0.0179
                                                Root MSE          =     .36374

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        scst |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   post3_trt |  -.1065707   .0559486    -1.90   0.072    -.2236724     .010531
       post3 |   .1230185   .0311538     3.95   0.001     .0578129     .188224
         trt |   .1069733   .0290455     3.68   0.002     .0461805    .1677662
       _cons |   .0637584   .0107169     5.95   0.000     .0413276    .0861892
------------------------------------------------------------------------------
tfw

Linear regression                               Number of obs     =        957
                                                F(1, 12)          =       2.36
                                                Prob > F          =     0.1502
                                                R-squared         =     0.0080
                                                Root MSE          =     .49194

                                (Std. Err. adjusted for 13 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tfw |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |  -.0935331   .0608448    -1.54   0.150    -.2261026    .0390364
       _cons |   .6435331   .0538915    11.94   0.000     .5261136    .7609527
------------------------------------------------------------------------------

Linear regression                               Number of obs     =        598
                                                F(1, 6)           =       1.47
                                                Prob > F          =     0.2715
                                                R-squared         =     0.0014
                                                Root MSE          =     .49873

                                 (Std. Err. adjusted for 7 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tfw |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       post3 |  -.0409303   .0338077    -1.21   0.272    -.1236547    .0417942
       _cons |   .5706215   .0209212    27.27   0.000     .5194292    .6218137
------------------------------------------------------------------------------

Linear regression                               Number of obs     =      1,555
                                                F(3, 19)          =       1.36
                                                Prob > F          =     0.2844
                                                R-squared         =     0.0069
                                                Root MSE          =     .49456

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tfw |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   post3_trt |   .0526029   .0680578     0.77   0.449    -.0898438    .1950496
       post3 |  -.0935331    .060003    -1.56   0.136    -.2191209    .0320547
         trt |  -.0729117   .0567407    -1.28   0.214    -.1916712    .0458479
       _cons |   .6435331   .0531459    12.11   0.000     .5322974    .7547688
------------------------------------------------------------------------------

. 
. 
. 
. 
. 
. ****************************************************************************
. ** TABLE 3: CHAIN ENTRY, CUSTOMER TRAFFIC, AND MARKET EXIT FOR INCUMBENTS **
. ****************************************************************************
. 
. use traffic, clear      

. areg lntr r2 r2_trt r3 r3_trt                , cl(mkt_id) ab(mkt_id)

Linear regression, absorbing indicators         Number of obs     =        297
                                                F(   4,     19)   =      10.24
                                                Prob > F          =     0.0001
                                                R-squared         =     0.2744
                                                Adj R-squared     =     0.2133
                                                Root MSE          =     0.3494

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        lntr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |   .1908671   .0523573     3.65   0.002      .081282    .3004521
      r2_trt |  -.2716958   .0690161    -3.94   0.001    -.4161482   -.1272434
          r3 |   .2645732   .0453124     5.84   0.000     .1697332    .3594132
      r3_trt |    -.24185   .0806762    -3.00   0.007    -.4107072   -.0729928
       _cons |   4.185973   .0227609   183.91   0.000     4.138334    4.233612
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg lntr r2 r2_trt r3 r3_trt $demo_m $hlth_m, cl(mkt_id) ab(mkt_id)

Linear regression, absorbing indicators         Number of obs     =        297
                                                F(  13,     19)   =      14.79
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2900
                                                Adj R-squared     =     0.2039
                                                Root MSE          =     0.3515

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        lntr |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |   .1144806   .0587471     1.95   0.066    -.0084784    .2374397
      r2_trt |  -.1962165   .0837281    -2.34   0.030    -.3714614   -.0209717
          r3 |    .195657   .1992434     0.98   0.338    -.2213642    .6126782
      r3_trt |  -.1892446   .0756893    -2.50   0.022    -.3476641   -.0308252
       inc_m |   .0123372   .0075707     1.63   0.120    -.0035085    .0281828
       edu_m |  -.0363561   .0246623    -1.47   0.157    -.0879749    .0152627
       deg_m |  -.0214982   .1970913    -0.11   0.914    -.4340151    .3910186
       cst_m |   .0746092   .1881127     0.40   0.696    -.3191152    .4683336
       tfw_m |   -.135711   .1352103    -1.00   0.328    -.4187093    .1472874
      fevr_m |  -1.047474    1.04817    -1.00   0.330    -3.241319    1.146371
      diar_m |   1.096249   4.853826     0.23   0.824    -9.062926    11.25542
      cold_m |  -.8159951   .7795335    -1.05   0.308    -2.447578    .8155873
      injy_m |   5.115722   1.677657     3.05   0.007     1.604345    8.627098
       _cons |   4.975656    .590859     8.42   0.000     3.738974    6.212338
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. psroutine_3round lntr "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.72327
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.27170                   0.274
Controlled   |       -0.19622                   0.290
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.321
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.85496
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.24185                   0.274
Controlled   |       -0.18924                   0.290
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.321
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. 
. use census, clear

. areg present r2 r2_trt r3 r3_trt                , cl(mkt_id) ab(mkt_id)

Linear regression, absorbing indicators         Number of obs     =      1,053
                                                F(   4,     19)   =       9.10
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0510
                                                Adj R-squared     =     0.0297
                                                Root MSE          =     0.1613

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
     present |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.0210084    .009944    -2.11   0.048    -.0418215   -.0001954
      r2_trt |  -.0055403   .0158515    -0.35   0.731    -.0387179    .0276373
          r3 |  -.0420168   .0134165    -3.13   0.005    -.0700978   -.0139358
      r3_trt |  -.0553283   .0257877    -2.15   0.045    -.1093026   -.0013541
       _cons |          1      .0052   192.31   0.000     .9891162    1.010884
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg present r2 r2_trt r3 r3_trt $demo_m $hlth_m, cl(mkt_id) ab(mkt_id)

Linear regression, absorbing indicators         Number of obs     =      1,053
                                                F(  13,     19)   =      25.42
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0550
                                                Adj R-squared     =     0.0253
                                                Root MSE          =     0.1616

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
     present |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.0309779   .0145961    -2.12   0.047    -.0615279   -.0004279
      r2_trt |   .0063666    .021304     0.30   0.768    -.0382233    .0509565
          r3 |  -.0860916   .0327668    -2.63   0.017    -.1546734   -.0175099
      r3_trt |  -.0470282   .0259302    -1.81   0.086    -.1013007    .0072442
       inc_m |   .0024919   .0031012     0.80   0.432     -.003999    .0089828
       edu_m |   -.001793   .0071463    -0.25   0.805    -.0167504    .0131644
       deg_m |    .016215   .0566278     0.29   0.778    -.1023083    .1347383
       cst_m |   .1149353    .037182     3.09   0.006     .0371125    .1927581
       tfw_m |  -.0217968   .0366985    -0.59   0.560    -.0986076     .055014
      fevr_m |  -.5913255   .3359246    -1.76   0.094    -1.294424    .1117727
      diar_m |   1.455857   .8654895     1.68   0.109    -.3556333    3.267347
      cold_m |   .0791865   .1961616     0.40   0.691    -.3313844    .4897575
      injy_m |  -.0521641    .928579    -0.06   0.956    -1.995702    1.891374
       _cons |   1.033688   .1808975     5.71   0.000     .6550649    1.412311
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. psroutine_3round present "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -0.06954
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.00554                   0.051
Controlled   |        0.00637                   0.055
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.080
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.31360
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.05533                   0.051
Controlled   |       -0.04703                   0.055
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.080
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. 
. 
. 
. *****************************************************
. ** TABLE 4: CHAIN ENTRY AND INCUMBENT DRUG QUALITY **
. *****************************************************
. 
. use final, clear

. areg pass post post_trt,                 cl(mktid) ab(mkt_id), if $urc & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(   2,     19)   =       2.97
                                                Prob > F          =     0.0756
                                                R-squared         =     0.0629
                                                Adj R-squared     =     0.0371
                                                Root MSE          =     0.2024

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0419044   .0190105    -2.20   0.040    -.0816938    -.002115
    post_trt |   .0426535   .0190242     2.24   0.037     .0028354    .0824716
       _cons |   .9691866   .0062576   154.88   0.000     .9560893    .9822839
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt $demo_m $hlth_m, cl(mktid) ab(mkt_id), if $urc & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(  11,     19)   =      12.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0741
                                                Adj R-squared     =     0.0374
                                                Root MSE          =     0.2024

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0773276   .0128675    -6.01   0.000    -.1042596   -.0503956
    post_trt |   .0634173   .0168362     3.77   0.001     .0281786     .098656
       inc_m |   2.52e-06   3.64e-06     0.69   0.497    -5.10e-06    .0000102
       edu_m |   .0074841   .0064254     1.16   0.259    -.0059644    .0209326
       deg_m |   -.214483   .0903231    -2.37   0.028    -.4035314   -.0254345
       cst_m |  -.0762391   .1096471    -0.70   0.495    -.3057331     .153255
       tfw_m |  -.0529202   .0658699    -0.80   0.432    -.1907876    .0849471
      fevr_m |   -.185342   .5946218    -0.31   0.759      -1.4299    1.059216
      diar_m |   2.221164     .74045     3.00   0.007     .6713842    3.770943
      cold_m |  -.4328544   .2914475    -1.49   0.154    -1.042861    .1771522
      injy_m |  -2.043262   .4349499    -4.70   0.000    -2.953622   -1.132901
       _cons |   1.198258   .3147571     3.81   0.001     .5394635    1.857052
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt,                 cl(mktid) ab(mkt_id), if $urc & nn==0 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        525
                                                F(   2,     19)   =       1.23
                                                Prob > F          =     0.3134
                                                R-squared         =     0.0620
                                                Adj R-squared     =     0.0228
                                                Root MSE          =     0.1701

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0039926     .02038    -0.20   0.847    -.0466485    .0386632
    post_trt |  -.0160209    .024087    -0.67   0.514    -.0664355    .0343938
       _cons |   .9742934   .0068254   142.74   0.000     .9600076    .9885792
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt $demo_m $hlth_m, cl(mktid) ab(mkt_id), if $urc & nn==0 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        525
                                                F(  11,     19)   =      11.57
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0830
                                                Adj R-squared     =     0.0273
                                                Root MSE          =     0.1697

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0532336   .0191793    -2.78   0.012    -.0933764   -.0130909
    post_trt |  -.0007443   .0213637    -0.03   0.973     -.045459    .0439704
       inc_m |    .000012   4.38e-06     2.73   0.013     2.81e-06    .0000211
       edu_m |  -.0104189   .0084095    -1.24   0.230    -.0280202    .0071825
       deg_m |   .0461318    .076191     0.61   0.552    -.1133378    .2056013
       cst_m |  -.0345835   .0840726    -0.41   0.685    -.2105495    .1413825
       tfw_m |   .1164042   .0558357     2.08   0.051    -.0004613    .2332697
      fevr_m |   1.459873   .3598064     4.06   0.001     .7067897    2.212957
      diar_m |   5.286375   .7146567     7.40   0.000     3.790581    6.782169
      cold_m |  -.3420376   .2307642    -1.48   0.155    -.8250327    .1409575
      injy_m |  -.5117387   .6747746    -0.76   0.458    -1.924058    .9005807
       _cons |   .4646916   .2663847     1.74   0.097     -.092858    1.022241
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt,                 cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   2,     19)   =       4.53
                                                Prob > F          =     0.0247
                                                R-squared         =     0.2517
                                                Adj R-squared     =     0.1863
                                                Root MSE          =     0.2344

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1532151   .0606975    -2.52   0.021    -.2802564   -.0261737
    post_trt |   .2120689     .07055     3.01   0.007     .0644059    .3597318
       _cons |   .9715333   .0224513    43.27   0.000     .9245422    1.018524
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt $demo_m $hlth_m, cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(  11,     19)   =      24.92
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3485
                                                Adj R-squared     =     0.2639
                                                Root MSE          =     0.2229

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.2270381   .0658306    -3.45   0.003    -.3648232    -.089253
    post_trt |   .2388807   .0500726     4.77   0.000     .1340775    .3436838
       inc_m |  -.0000154   .0000113    -1.36   0.188     -.000039    8.23e-06
       edu_m |   .0605152   .0237485     2.55   0.020     .0108091    .1102213
       deg_m |  -1.200313   .3434438    -3.49   0.002    -1.919149   -.4814771
       cst_m |  -.0825934   .3774185    -0.22   0.829    -.8725394    .7073526
       tfw_m |  -.5674777   .2058745    -2.76   0.013    -.9983779   -.1365775
      fevr_m |  -3.061267   1.774347    -1.73   0.101    -6.775017    .6524838
      diar_m |   .0240453   2.224118     0.01   0.991    -4.631088    4.679178
      cold_m |  -.8956487    1.09219    -0.82   0.422    -3.181629    1.390332
      injy_m |  -8.912403   2.390035    -3.73   0.001     -13.9148   -3.910002
       _cons |   3.272576   .9593499     3.41   0.003     1.264634    5.280519
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt $demo_m $hlth_m mk_*, cl(mktid) ab(manuf), if $urc & nn==1 & ln_ppt~=.
note: mk_20 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(  19,     19)   =          .
                                                Prob > F          =          .
                                                R-squared         =     0.5323
                                                Adj R-squared     =     0.3957
                                                Root MSE          =     0.1967

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   -.143661   .0817683    -1.76   0.095     -.314804    .0274821
    post_trt |    .175864      .0815     2.16   0.044     .0052825    .3464455
       inc_m |  -.0000225   .0000186    -1.21   0.241    -.0000615    .0000164
       edu_m |   .1064734   .0387271     2.75   0.013     .0254167    .1875301
       deg_m |  -.9690845   .5691726    -1.70   0.105    -2.160376    .2222074
       cst_m |   .4154535   .5492284     0.76   0.459    -.7340947    1.565002
       tfw_m |  -.1164458   .3572985    -0.33   0.748    -.8642802    .6313886
      fevr_m |  -2.782702   2.834649    -0.98   0.339     -8.71569    3.150286
      diar_m |   1.206995   2.966501     0.41   0.689    -5.001964    7.415954
      cold_m |    1.06983   1.624011     0.66   0.518    -2.329264    4.468925
      injy_m |  -7.000066   3.358819    -2.08   0.051    -14.03016     .030023
        mk_1 |   -.197321   .1019574    -1.94   0.068    -.4107202    .0160783
        mk_2 |  -.0566045   .1017806    -0.56   0.585    -.2696338    .1564249
        mk_3 |   -.270341   .1025343    -2.64   0.016    -.4849477   -.0557343
        mk_4 |   .0909016    .199688     0.46   0.654    -.3270501    .5088533
        mk_5 |  -.1633907   .0995495    -1.64   0.117    -.3717503    .0449689
        mk_6 |  -.2940624   .1272952    -2.31   0.032    -.5604943   -.0276306
        mk_7 |   -.208495   .1617048    -1.29   0.213    -.5469471    .1299571
        mk_8 |  -.0220105   .0761284    -0.29   0.776    -.1813491     .137328
        mk_9 |   -.079107   .1729718    -0.46   0.653    -.4411411    .2829271
       mk_10 |  -.2110387   .1554755    -1.36   0.191    -.5364527    .1143753
       mk_11 |  -.1106644    .192251    -0.58   0.572    -.5130505    .2917216
       mk_12 |  -.2449252   .0902697    -2.71   0.014    -.4338618   -.0559887
       mk_13 |  -.3532682   .1401993    -2.52   0.021    -.6467087   -.0598276
       mk_14 |  -.3967099   .1242408    -3.19   0.005    -.6567489   -.1366709
       mk_15 |  -.2088835   .1213235    -1.72   0.101    -.4628164    .0450494
       mk_16 |  -.0593042   .1191046    -0.50   0.624    -.3085929    .1899845
       mk_17 |  -.1562122   .1318474    -1.18   0.251    -.4321719    .1197475
       mk_18 |  -.1545782   .0898117    -1.72   0.101    -.3425562    .0333998
       mk_19 |   -.097572   .0736437    -1.32   0.201      -.25171    .0565659
       mk_20 |          0  (omitted)
       _cons |   1.104773   1.672498     0.66   0.517    -2.395806    4.605352
-------------+----------------------------------------------------------------
       manuf |   absorbed                                      (30 categories)

. 
. 
. ****************************************
. ** PROPORTIONAL SELECTION FOR TABLE 4 **
. ****************************************
. 
. // COLUMN 2
. use if $urc & ln_ppt~=. using final, clear

. psroutine pass "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      31.21538
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.04265                   0.063
Controlled   |        0.06342                   0.074
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.079
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. // COLUMN 4
. use if $urc & nn==0 & ln_ppt~=. using final, clear

. psroutine pass "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.07141
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01602                   0.062
Controlled   |       -0.00074                   0.083
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.091
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. // COLUMN 6
. use if $urc & nn==1 & ln_ppt~=. using final, clear

. psroutine pass "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       4.99726
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.21207                   0.252
Controlled   |        0.23888                   0.348
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.382
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. **************************************************
. ** TABLE 5: THE IMPACT OF CHAIN ENTRY ON PRICES **
. **************************************************
. 
. use final, clear

. 
. areg ln_ppt post post_trt,                 cl(mktid) ab(mkt_id), if $urc

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(   2,     19)   =       0.54
                                                Prob > F          =     0.5908
                                                R-squared         =     0.1083
                                                Adj R-squared     =     0.0838
                                                Root MSE          =     0.2101

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0050683   .0247469     0.20   0.840    -.0467277    .0568642
    post_trt |   -.024203   .0310536    -0.78   0.445     -.089199     .040793
       _cons |  -1.850379   .0087834  -210.67   0.000    -1.868763   -1.831995
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt $demo_m $hlth_m, cl(mktid) ab(mkt_id), if $urc

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(  11,     19)   =       1.02
                                                Prob > F          =     0.4640
                                                R-squared         =     0.1147
                                                Adj R-squared     =     0.0796
                                                Root MSE          =     0.2106

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0192767   .0301439     0.64   0.530    -.0438153    .0823687
    post_trt |  -.0385098     .03987    -0.97   0.346    -.1219587    .0449391
       inc_m |  -3.39e-06   7.99e-06    -0.42   0.677    -.0000201    .0000133
       edu_m |  -.0057139   .0183543    -0.31   0.759      -.04413    .0327022
       deg_m |  -.0407099   .1745532    -0.23   0.818    -.4060539     .324634
       cst_m |   .0050376   .1914198     0.03   0.979    -.3956088    .4056839
       tfw_m |  -.1464073   .0924909    -1.58   0.130    -.3399929    .0471783
      fevr_m |  -.2099853   1.166095    -0.18   0.859    -2.650651     2.23068
      diar_m |  -1.181446   1.791989    -0.66   0.518    -4.932123     2.56923
      cold_m |  -.2545531   .6781728    -0.38   0.712    -1.673985    1.164879
      injy_m |  -.8982754   1.061763    -0.85   0.408    -3.120571     1.32402
       _cons |  -1.189337   .4688849    -2.54   0.020    -2.170724   -.2079497
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt,                 cl(mktid) ab(mkt_id), if $urc & nn==0

Linear regression, absorbing indicators         Number of obs     =        525
                                                F(   2,     19)   =       0.26
                                                Prob > F          =     0.7707
                                                R-squared         =     0.1336
                                                Adj R-squared     =     0.0974
                                                Root MSE          =     0.1994

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0150437    .020949    -0.72   0.481    -.0588906    .0288031
    post_trt |   .0180304   .0340804     0.53   0.603    -.0533008    .0893615
       _cons |  -1.809824   .0081281  -222.66   0.000    -1.826836   -1.792812
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt $demo_m $hlth_m, cl(mktid) ab(mkt_id), if $urc & nn==0

Linear regression, absorbing indicators         Number of obs     =        525
                                                F(  11,     19)   =       1.60
                                                Prob > F          =     0.1764
                                                R-squared         =     0.1395
                                                Adj R-squared     =     0.0872
                                                Root MSE          =     0.2005

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0203929   .0322424    -0.63   0.535     -.087877    .0470912
    post_trt |   .0308795   .0469058     0.66   0.518    -.0672956    .1290546
       inc_m |   3.94e-06   7.14e-06     0.55   0.587     -.000011    .0000189
       edu_m |   .0014891   .0171229     0.09   0.932    -.0343496    .0373278
       deg_m |  -.0183648   .1563635    -0.12   0.908    -.3456373    .3089077
       cst_m |   -.129476   .1897754    -0.68   0.503    -.5266804    .2677284
       tfw_m |  -.0382566   .0920345    -0.42   0.682     -.230887    .1543738
      fevr_m |   .0640063   1.157645     0.06   0.956    -2.358972    2.486984
      diar_m |  -.4062528   2.180954    -0.19   0.854    -4.971042    4.158537
      cold_m |  -.6375542   .7276437    -0.88   0.392     -2.16053    .8854215
      injy_m |  -1.212688   1.125183    -1.08   0.295    -3.567724    1.142347
       _cons |  -1.671124   .4884862    -3.42   0.003    -2.693537   -.6487106
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt,                 cl(mktid) ab(mkt_id), if $urc & nn==1

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   2,     19)   =       4.75
                                                Prob > F          =     0.0212
                                                R-squared         =     0.1988
                                                Adj R-squared     =     0.1287
                                                Root MSE          =     0.2045

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0513909   .0511926     1.00   0.328    -.0557565    .1585383
    post_trt |  -.1101736   .0550217    -2.00   0.060    -.2253353    .0049882
       _cons |  -1.936152   .0185132  -104.58   0.000      -1.9749   -1.897403
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt $demo_m $hlth_m, cl(mktid) ab(mkt_id), if $urc & nn==1

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(  11,     19)   =      11.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2569
                                                Adj R-squared     =     0.1604
                                                Root MSE          =     0.2007

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0762117   .0573076     1.33   0.199    -.0437344    .1961578
    post_trt |  -.1387328   .0733191    -1.89   0.074    -.2921914    .0147258
       inc_m |  -.0000243   .0000165    -1.48   0.156    -.0000588    .0000102
       edu_m |  -.0078209   .0433566    -0.18   0.859    -.0985673    .0829254
       deg_m |  -.3612968    .255043    -1.42   0.173    -.8951079    .1725143
       cst_m |  -.7567373   .5801099    -1.30   0.208    -1.970921    .4574466
       tfw_m |  -.5579769   .1653124    -3.38   0.003    -.9039797   -.2119742
      fevr_m |  -3.096118   1.985663    -1.56   0.135    -7.252158    1.059921
      diar_m |  -8.448081   2.613871    -3.23   0.004    -13.91898   -2.977186
      cold_m |  -2.005692   1.478306    -1.36   0.191    -5.099823    1.088439
      injy_m |    .898645    1.98517     0.45   0.656    -3.256364    5.053654
       _cons |   .8907727    1.01367     0.88   0.391    -1.230863    3.012409
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt $demo_m $hlth_m mk_*, cl(mktid) ab(manuf), if $urc & nn==1
note: mk_20 omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(  19,     19)   =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6616
                                                Adj R-squared     =     0.5627
                                                Root MSE          =     0.1411

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0941405   .0778798     1.21   0.242    -.0688639    .2571448
    post_trt |  -.1424095    .081011    -1.76   0.095    -.3119675    .0271484
       inc_m |  -2.28e-06   .0000167    -0.14   0.893    -.0000373    .0000327
       edu_m |  -.0123005   .0467012    -0.26   0.795    -.1100472    .0854463
       deg_m |  -.0204828   .3440399    -0.06   0.953    -.7405666    .6996009
       cst_m |  -.3347113   .6377125    -0.52   0.606    -1.669459    1.000036
       tfw_m |   -.171966   .1451232    -1.18   0.251    -.4757124    .1317805
      fevr_m |  -1.991393   2.015263    -0.99   0.335    -6.209388    2.226601
      diar_m |    -5.6231   2.987254    -1.88   0.075    -11.87549    .6292935
      cold_m |   .2089949   1.752761     0.12   0.906    -3.459576    3.877566
      injy_m |   .7961192   2.108798     0.38   0.710    -3.617645    5.209883
        mk_1 |   .1432301   .1043435     1.37   0.186    -.0751634    .3616236
        mk_2 |   .0816353   .0943604     0.87   0.398    -.1158633    .2791339
        mk_3 |   .0750803   .0698345     1.08   0.296     -.071085    .2212456
        mk_4 |   .1581862   .2467794     0.64   0.529    -.3583291    .6747015
        mk_5 |    .015155   .1343849     0.11   0.911    -.2661159    .2964258
        mk_6 |   .0206619   .0666748     0.31   0.760      -.11889    .1602138
        mk_7 |  -.0072845   .0762318    -0.10   0.925    -.1668394    .1522705
        mk_8 |    -.05693   .0728985    -0.78   0.444    -.2095083    .0956484
        mk_9 |  -.0477159   .0977408    -0.49   0.631    -.2522898    .1568579
       mk_10 |  -.1465092   .1239929    -1.18   0.252    -.4060293     .113011
       mk_11 |  -.0745976   .1324868    -0.56   0.580    -.3518956    .2027005
       mk_12 |  -.0058146   .0479695    -0.12   0.905    -.1062158    .0945867
       mk_13 |   .0829848   .1709738     0.49   0.633    -.2748674     .440837
       mk_14 |  -.0610827   .0872502    -0.70   0.492    -.2436995    .1215341
       mk_15 |    .119722   .1209499     0.99   0.335     -.133429     .372873
       mk_16 |   .0021656    .077582     0.03   0.978    -.1602154    .1645465
       mk_17 |  -.0524961   .0560355    -0.94   0.361    -.1697797    .0647875
       mk_18 |  -.0132731   .0448014    -0.30   0.770    -.1070435    .0804973
       mk_19 |  -.1959658   .0706984    -2.77   0.012    -.3439391   -.0479924
       mk_20 |          0  (omitted)
       _cons |  -1.000149   .9674248    -1.03   0.314    -3.024992    1.024695
-------------+----------------------------------------------------------------
       manuf |   absorbed                                      (30 categories)

. 
. 
. ****************************************
. ** PROPORTIONAL SELECTION FOR TABLE 5 **
. ****************************************
. 
. // COLUMN 2
. use if $urc using final, clear

. psroutine ln_ppt "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -8.14879
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.02420                   0.108
Controlled   |       -0.03851                   0.115
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.137
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. // COLUMN 4
. use if $urc & nn==0 using final, clear

. psroutine ln_ppt "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |     -13.72277
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.01803                   0.134
Controlled   |        0.03088                   0.139
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.163
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. // COLUMN 6
. use if $urc & nn==1 using final, clear

. psroutine ln_ppt "$demo_m $hlth_m"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       4.80041
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.11017                   0.199
Controlled   |       -0.13873                   0.257
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.306
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. 
. 
. ************************************************
. ** TABLE 6: CHAIN ENTRY AND PERCEIVED QUALITY **
. ************************************************
. 
. use consumer, clear

. areg qual_nearpharms r2 r3 r2_trt r3_trt [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2 & mp==0
(sum of wgt is   8.8522e+05)

Linear regression, absorbing indicators         Number of obs     =      2,143
                                                F(   4,     19)   =       5.24
                                                Prob > F          =     0.0051
                                                R-squared         =     0.0465
                                                Adj R-squared     =     0.0361
                                                Root MSE          =     0.4704

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
qual_nearp~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.1763565   .0408223    -4.32   0.000    -.2617986   -.0909145
          r3 |  -.0690732   .0390259    -1.77   0.093    -.1507554     .012609
      r2_trt |   .0814698   .0948201     0.86   0.401     -.116991    .2799306
      r3_trt |   .0991952   .0459897     2.16   0.044     .0029377    .1954527
       _cons |   3.011174   .0222887   135.10   0.000     2.964523    3.057825
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg qual_nearpharms r2 r3 r2_trt r3_trt $demo_m $hlth_m [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2 & mp==0
(sum of wgt is   8.8522e+05)

Linear regression, absorbing indicators         Number of obs     =      2,143
                                                F(  13,     19)   =      22.10
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0545
                                                Adj R-squared     =     0.0401
                                                Root MSE          =     0.4694

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
qual_nearp~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |   -.295633   .0513539    -5.76   0.000     -.403118   -.1881479
          r3 |  -.0895273   .2580341    -0.35   0.732    -.6295989    .4505444
      r2_trt |   .1910652   .1109392     1.72   0.101    -.0411331    .4232635
      r3_trt |    .174224   .0647841     2.69   0.015     .0386293    .3098187
       inc_m |   .0018045   .0085874     0.21   0.836    -.0161692    .0197782
       edu_m |   .0120155   .0362396     0.33   0.744    -.0638349     .087866
       deg_m |  -.2249595   .3337081    -0.67   0.508    -.9234186    .4734996
       cst_m |   .3909813   .1797992     2.17   0.043     .0146572    .7673053
       tfw_m |  -.0913963    .166879    -0.55   0.590     -.440678    .2578855
      fevr_m |  -.4065394   .8689118    -0.47   0.645    -2.225193    1.412114
      diar_m |   9.857131   3.195676     3.08   0.006     3.168503    16.54576
      cold_m |  -1.220362   .8938318    -1.37   0.188    -3.091174    .6504494
      injy_m |  -.6115061   2.290799    -0.27   0.792    -5.406204    4.183191
       _cons |   3.131777   .7852591     3.99   0.001     1.488211    4.775343
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg qual_natdrugs   r2 r3 r2_trt r3_trt [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2 & mp==0
(sum of wgt is   6.8568e+05)

Linear regression, absorbing indicators         Number of obs     =      1,677
                                                F(   4,     19)   =      10.38
                                                Prob > F          =     0.0001
                                                R-squared         =     0.1122
                                                Adj R-squared     =     0.0998
                                                Root MSE          =     0.6204

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
qual_natdr~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.3615757   .1275107    -2.84   0.011    -.6284586   -.0946927
          r3 |  -.1516873   .0880029    -1.72   0.101    -.3358796    .0325049
      r2_trt |   .1901797    .203309     0.94   0.361    -.2353509    .6157103
      r3_trt |  -.1274583   .1018897    -1.25   0.226    -.3407159    .0857993
       _cons |   3.405657   .0485423    70.16   0.000     3.304057    3.507257
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg qual_natdrugs   r2 r3 r2_trt r3_trt $demo_m $hlth_m [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2 & mp==0
(sum of wgt is   6.8568e+05)

Linear regression, absorbing indicators         Number of obs     =      1,677
                                                F(  13,     19)   =      18.78
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1578
                                                Adj R-squared     =     0.1414
                                                Root MSE          =     0.6059

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
qual_natdr~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.6137947   .0859185    -7.14   0.000    -.7936243   -.4339652
          r3 |  -.6595457   .5390702    -1.22   0.236    -1.787833    .4687411
      r2_trt |   .4949767   .2120995     2.33   0.031     .0510473    .9389061
      r3_trt |   .1030697   .1586234     0.65   0.524    -.2289329    .4350722
       inc_m |   .0034543   .0108631     0.32   0.754    -.0192824    .0261911
       edu_m |  -.0065442   .0567815    -0.12   0.909    -.1253893     .112301
       deg_m |   .2039081   .6522362     0.31   0.758    -1.161238    1.569054
       cst_m |   .3139036   .3623463     0.87   0.397     -.444496    1.072303
       tfw_m |  -.1974222   .3983378    -0.50   0.626    -1.031153    .6363084
      fevr_m |   1.827461   1.755793     1.04   0.311    -1.847457    5.502378
      diar_m |   24.22096   8.637925     2.80   0.011     6.141571    42.30034
      cold_m |  -8.542334   2.129521    -4.01   0.001    -12.99947   -4.085196
      injy_m |  -9.360544   6.132633    -1.53   0.143    -22.19629    3.475206
       _cons |   4.271382   1.526351     2.80   0.011     1.076693    7.466071
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg qual_locdrugs   r2 r3 r2_trt r3_trt [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2 & mp==0
(sum of wgt is   6.0930e+05)

Linear regression, absorbing indicators         Number of obs     =      1,505
                                                F(   4,     19)   =      11.88
                                                Prob > F          =     0.0001
                                                R-squared         =     0.1851
                                                Adj R-squared     =     0.1724
                                                Root MSE          =     0.7069

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
qual_locdr~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.8439946   .1997382    -4.23   0.000    -1.262051   -.4259378
          r3 |  -.5013399   .1089198    -4.60   0.000    -.7293116   -.2733681
      r2_trt |   .3668927   .2586372     1.42   0.172    -.1744412    .9082266
      r3_trt |   .2845091   .1350356     2.11   0.049     .0018764    .5671419
       _cons |   2.800156   .0611709    45.78   0.000     2.672124    2.928188
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg qual_locdrugs   r2 r3 r2_trt r3_trt $demo_m $hlth_m [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2 & mp==0
(sum of wgt is   6.0930e+05)

Linear regression, absorbing indicators         Number of obs     =      1,505
                                                F(  13,     19)   =      36.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2321
                                                Adj R-squared     =     0.2154
                                                Root MSE          =     0.6883

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
qual_locdr~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -1.017277   .1205087    -8.44   0.000    -1.269504   -.7650491
          r3 |  -1.238489   .5074417    -2.44   0.025    -2.300576    -.176401
      r2_trt |   .6170733   .2631486     2.34   0.030      .066297     1.16785
      r3_trt |   .5248214   .1251617     4.19   0.000     .2628549    .7867878
       inc_m |   .0036685   .0226445     0.16   0.873    -.0437271    .0510641
       edu_m |  -.1031565   .0783551    -1.32   0.204    -.2671556    .0608425
       deg_m |   .1820399    .656478     0.28   0.785    -1.191984    1.556064
       cst_m |   1.826226    .443505     4.12   0.001      .897959    2.754492
       tfw_m |  -.7596835   .4190624    -1.81   0.086    -1.636791    .1174241
      fevr_m |  -2.081855   2.970653    -0.70   0.492    -8.299504    4.135793
      diar_m |   16.25318   8.037517     2.02   0.057     -.569537     33.0759
      cold_m |  -2.977768   2.177838    -1.37   0.187    -7.536035    1.580498
      injy_m |  -4.907701   5.673858    -0.86   0.398    -16.78322     6.96782
       _cons |   5.804363   1.931673     3.00   0.007     1.761325      9.8474
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. ****************************************
. ** PROPORTIONAL SELECTION FOR TABLE 6 **
. ****************************************
. 
. use if int_loc==2 & mp==0 using consumer, clear

. 
. // COLUMN 2
. psroutine_3round qual_nearpharms "$demo_m $hlth_m" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -0.42946
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.08147                   0.046
Controlled   |        0.19107                   0.054
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.084
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -0.75976
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.09920                   0.046
Controlled   |        0.17422                   0.054
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.084
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. 
. // COLUMN 4
. psroutine_3round qual_natdrugs   "$demo_m $hlth_m" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -1.37639
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.19018                   0.112
Controlled   |        0.49498                   0.158
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.223
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -0.24765
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.12746                   0.112
Controlled   |        0.10307                   0.158
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.223
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. 
. // COLUMN 6
. psroutine_3round qual_locdrugs   "$demo_m $hlth_m" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      32.31041
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.36689                   0.185
Controlled   |        0.61707                   0.232
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.293
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -3.73437
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.28451                   0.185
Controlled   |        0.52482                   0.232
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.293
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. 
. 
. ****************************************************
. ** TABLE 7: THE MARKET-WIDE IMPACT OF CHAIN ENTRY **
. ****************************************************
. 
. use final, clear

. areg pass post post_trt [pw=tr_all], cl(mktid) ab(mkt_id),   if tag_unitround==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(   2,     19)   =       2.79
                                                Prob > F          =     0.0869
                                                R-squared         =     0.0573
                                                Adj R-squared     =     0.0324
                                                Root MSE          =     0.2061

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0496945   .0211945    -2.34   0.030     -.094055    -.005334
    post_trt |   .0486074    .021573     2.25   0.036     .0034545    .0937602
       _cons |   .9724925   .0078147   124.44   0.000     .9561362    .9888487
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt $demo_m $hlth_m  [pw=tr_all], cl(mktid) ab(mkt_id),   if tag_unitround==1 & ln_ppt~=. 

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(  11,     19)   =       9.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0691
                                                Adj R-squared     =     0.0335
                                                Root MSE          =     0.2060

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0907337   .0154488    -5.87   0.000    -.1230685   -.0583989
    post_trt |    .067831   .0213019     3.18   0.005     .0232456    .1124165
       inc_m |   3.01e-06   4.47e-06     0.67   0.509    -6.34e-06    .0000124
       edu_m |   .0042888   .0083454     0.51   0.613    -.0131783    .0217559
       deg_m |  -.2320494   .1102722    -2.10   0.049    -.4628518    -.001247
       cst_m |  -.1465905   .1351399    -1.08   0.292    -.4294415    .1362606
       tfw_m |   -.043957   .0789648    -0.56   0.584    -.2092323    .1213183
      fevr_m |  -.0461073   .6998156    -0.07   0.948    -1.510838    1.418624
      diar_m |   2.375225   1.027338     2.31   0.032     .2249814    4.525469
      cold_m |  -.6515401   .3614408    -1.80   0.087    -1.408044    .1049643
      injy_m |  -2.262467   .5926918    -3.82   0.001    -3.502985   -1.021949
       _cons |   1.227203   .3757582     3.27   0.004     .4407321    2.013674
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post pe post_trt post_pe trt_pe post_trt_pe [pw=tr_all], cl(mktid) ab(mktid), if tag_unitround==1 & ln_ppt~=. 
(sum of wgt is   5.9745e+04)

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(   6,     19)   =       1.97
                                                Prob > F          =     0.1210
                                                R-squared         =     0.0578
                                                Adj R-squared     =     0.0279
                                                Root MSE          =     0.2065

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0465034   .0337034    -1.38   0.184    -.1170453    .0240386
          pe |   .0156687   .0210159     0.75   0.465    -.0283182    .0596555
    post_trt |   .0391264   .0342992     1.14   0.268    -.0326627    .1109154
     post_pe |  -.0060651   .0443911    -0.14   0.893    -.0989767    .0868465
      trt_pe |  -.0173598   .0347679    -0.50   0.623    -.0901298    .0554101
 post_trt_pe |   .0177155   .0473909     0.37   0.713    -.0814748    .1169058
       _cons |   .9667715   .0077982   123.97   0.000     .9504497    .9830933
-------------+----------------------------------------------------------------
       mktid |   absorbed                                      (20 categories)

. areg pass post he post_trt post_he trt_he post_trt_he [pw=tr_all], cl(mktid) ab(mktid), if tag_unitround==1 & ln_ppt~=. 
(sum of wgt is   5.9745e+04)

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(   6,     19)   =       1.90
                                                Prob > F          =     0.1326
                                                R-squared         =     0.0594
                                                Adj R-squared     =     0.0296
                                                Root MSE          =     0.2064

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0461632   .0277589    -1.66   0.113    -.1042633    .0119369
          he |  -.0059202   .0226852    -0.26   0.797    -.0534009    .0415605
    post_trt |    .054049   .0367841     1.47   0.158    -.0229411    .1310391
     post_he |  -.0071238   .0327673    -0.22   0.830    -.0757065     .061459
      trt_he |  -.0130545   .0437759    -0.30   0.769    -.1046784    .0785695
 post_trt_he |  -.0108662   .0643109    -0.17   0.868    -.1454705    .1237381
       _cons |   .9776204   .0113295    86.29   0.000     .9539075    1.001333
-------------+----------------------------------------------------------------
       mktid |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt [pw=tr_all], cl(mktid) ab(mkt_id),   if tag_unitround==1

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(   2,     19)   =       1.38
                                                Prob > F          =     0.2761
                                                R-squared         =     0.1338
                                                Adj R-squared     =     0.1108
                                                Root MSE          =     0.2093

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0223723   .0268057     0.83   0.414    -.0337326    .0784772
    post_trt |  -.0549447   .0351237    -1.56   0.134    -.1284595    .0185702
       _cons |  -1.865024   .0105838  -176.21   0.000    -1.887177   -1.842872
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt $demo_m $hlth_m [pw=tr_all], cl(mktid) ab(mkt_id),   if tag_unitround==1

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(  11,     19)   =       1.34
                                                Prob > F          =     0.2751
                                                R-squared         =     0.1443
                                                Adj R-squared     =     0.1115
                                                Root MSE          =     0.2092

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0186037   .0266484     0.70   0.494    -.0371722    .0743795
    post_trt |  -.0695074   .0354196    -1.96   0.065    -.1436414    .0046267
       inc_m |  -2.08e-07   9.62e-06    -0.02   0.983    -.0000203    .0000199
       edu_m |  -.0070277   .0177192    -0.40   0.696    -.0441144    .0300589
       deg_m |  -.1359981   .1855678    -0.73   0.473     -.524396    .2523997
       cst_m |   .0803249   .2060592     0.39   0.701     -.350962    .5116119
       tfw_m |   -.193638   .0914367    -2.12   0.048    -.3850173   -.0022588
      fevr_m |   .3285462   1.125933     0.29   0.774    -2.028059    2.685151
      diar_m |  -.9652433   1.881534    -0.51   0.614    -4.903339    2.972852
      cold_m |  -.3026125   .6797888    -0.45   0.661    -1.725427    1.120202
      injy_m |   -.461756    1.07852    -0.43   0.673    -2.719124    1.795612
       _cons |  -1.069065   .4675081    -2.29   0.034    -2.047571   -.0905593
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post pe post_trt post_pe trt_pe post_trt_pe [pw=tr_all], cl(mktid) ab(mktid), if tag_unitround==1
(sum of wgt is   5.9745e+04)

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(   6,     19)   =       4.00
                                                Prob > F          =     0.0093
                                                R-squared         =     0.1424
                                                Adj R-squared     =     0.1152
                                                Root MSE          =     0.2088

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0221127    .038777     0.57   0.575    -.0590484    .1032738
          pe |   .0281653   .0340668     0.83   0.419    -.0431373    .0994679
    post_trt |  -.0159487   .0399856    -0.40   0.694    -.0996394    .0677421
     post_pe |  -.0008607   .0442083    -0.02   0.985    -.0933896    .0916683
      trt_pe |   .0735267   .0481358     1.53   0.143    -.0272226    .1742761
 post_trt_pe |   -.085053   .0523248    -1.63   0.121    -.1945701    .0244641
       _cons |  -1.891331   .0169968  -111.28   0.000    -1.926905   -1.855756
-------------+----------------------------------------------------------------
       mktid |   absorbed                                      (20 categories)

. areg ln_ppt post he post_trt post_he trt_he post_trt_he [pw=tr_all], cl(mktid) ab(mktid), if tag_unitround==1
(sum of wgt is   5.9745e+04)

Linear regression, absorbing indicators         Number of obs     =        815
                                                F(   6,     19)   =       1.47
                                                Prob > F          =     0.2423
                                                R-squared         =     0.1373
                                                Adj R-squared     =     0.1100
                                                Root MSE          =     0.2094

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0172642   .0277093     0.62   0.541    -.0407321    .0752605
          he |   .0251144   .0141152     1.78   0.091    -.0044291    .0546579
    post_trt |  -.0581603   .0409903    -1.42   0.172     -.143954    .0276334
     post_he |   .0104346   .0173819     0.60   0.555    -.0259462    .0468153
      trt_he |  -.0399443   .0244289    -1.64   0.118    -.0910746     .011186
 post_trt_he |   .0061784   .0355793     0.17   0.864    -.0682899    .0806466
       _cons |  -1.871055   .0121393  -154.13   0.000    -1.896463   -1.845647
-------------+----------------------------------------------------------------
       mktid |   absorbed                                      (20 categories)

. 
. 
. ****************************************
. ** PROPORTIONAL SELECTION FOR TABLE 7 **
. ****************************************
. 
. use if tag_unitround==1 & ln_ppt~=. using final, clear

. 
. psroutine pass   "$demo_m $hlth_m" "[pw=tr_all]" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       8.19924
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.04861                   0.057
Controlled   |        0.06783                   0.069
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.078
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine ln_ppt "$demo_m $hlth_m" "[pw=tr_all]" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       1.73474
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.05494                   0.134
Controlled   |       -0.06951                   0.144
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.168
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. 
. ****************************************************
. ** TABLE 8: MECHANISMS FOR THE INCUMBENT RESPONSE **
. ****************************************************
. 
. use final, clear

. 
. areg national             post post_trt, cl(mkt_id) ab(mkt_id), if $urc & tte~=. & mpaspct~=.

Linear regression, absorbing indicators         Number of obs     =        740
                                                F(   2,     19)   =       0.09
                                                Prob > F          =     0.9177
                                                R-squared         =     0.0752
                                                Adj R-squared     =     0.0481
                                                Root MSE          =     0.4590

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
    national |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   -.020528   .0688225    -0.30   0.769    -.1645751    .1235191
    post_trt |   .0047017   .0879355     0.05   0.958    -.1793494    .1887528
       _cons |   .6794072   .0235599    28.84   0.000     .6300957    .7287186
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg mpaspct              post post_trt, cl(mkt_id) ab(mkt_id), if $urc & tte~=.

Linear regression, absorbing indicators         Number of obs     =        740
                                                F(   2,     19)   =       2.56
                                                Prob > F          =     0.1036
                                                R-squared         =     0.0524
                                                Adj R-squared     =     0.0246
                                                Root MSE          =     0.0736

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
     mpaspct |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0047384    .006287     0.75   0.460    -.0084204    .0178973
    post_trt |   .0146052   .0110328     1.32   0.201    -.0084867    .0376971
       _cons |   .9681528   .0025028   386.83   0.000     .9629144    .9733912
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg national_mpaspct post post_trt, cl(mkt_id) ab(mkt_id), if $urc & tte~=.

Linear regression, absorbing indicators         Number of obs     =        740
                                                F(   2,     19)   =       0.04
                                                Prob > F          =     0.9626
                                                R-squared         =     0.0756
                                                Adj R-squared     =     0.0485
                                                Root MSE          =     0.4508

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
national_m~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0162238   .0692477    -0.23   0.817    -.1611609    .1287134
    post_trt |   .0077371   .0902783     0.09   0.933    -.1812175    .1966917
       _cons |   .6606072   .0238983    27.64   0.000     .6105875    .7106269
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg tte                          post post_trt, cl(mkt_id) ab(mkt_id), if $urc & tte~=. & mpaspct~=.

Linear regression, absorbing indicators         Number of obs     =        740
                                                F(   2,     19)   =       0.14
                                                Prob > F          =     0.8733
                                                R-squared         =     0.0214
                                                Adj R-squared     =    -0.0073
                                                Root MSE          =   229.8794

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tte |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   6.796825    23.7102     0.29   0.777    -42.82919    56.42285
    post_trt |  -12.77565   27.37689    -0.47   0.646    -70.07614    44.52484
       _cons |   624.2016    7.81529    79.87   0.000      607.844    640.5592
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg degree                       post post_trt, cl(mkt_id) ab(mkt_id), if $prc & ph_include==1

Linear regression, absorbing indicators         Number of obs     =        198
                                                F(   2,     19)   =       3.93
                                                Prob > F          =     0.0374
                                                R-squared         =     0.2507
                                                Adj R-squared     =     0.1613
                                                Root MSE          =     0.3541

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
      degree |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |      -.125   .0610043    -2.05   0.055    -.2526834    .0026834
    post_trt |   .0107143   .0854049     0.13   0.901    -.1680402    .1894688
       _cons |   .8787879   .0223707    39.28   0.000     .8319654    .9256104
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ph_workers_tot   post post_trt, cl(mkt_id) ab(mkt_id), if $prc & ph_include==1

Linear regression, absorbing indicators         Number of obs     =        198
                                                F(   2,     19)   =       0.02
                                                Prob > F          =     0.9792
                                                R-squared         =     0.3202
                                                Adj R-squared     =     0.2391
                                                Root MSE          =     1.8715

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
ph_workers~t |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |    -.03125   .1960526    -0.16   0.875    -.4415927    .3790927
    post_trt |   .0026786   .2955538     0.01   0.993    -.6159226    .6212797
       _cons |   1.868687   .0744599    25.10   0.000     1.712841    2.024533
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ph_ac                post post_trt, cl(mkt_id) ab(mkt_id), if $prc & ph_include==1

Linear regression, absorbing indicators         Number of obs     =        198
                                                F(   1,     19)   =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2666
                                                Adj R-squared     =     0.1791
                                                Root MSE          =     0.2956

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       ph_ac |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |    -.03125   .0325515    -0.96   0.349    -.0993811    .0368811
    post_trt |     .03125   .0325515     0.96   0.349    -.0368811    .0993811
       _cons |   .1313131   .0105217    12.48   0.000      .109291    .1533353
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ph_shipments         post post_trt, cl(mkt_id) ab(mkt_id), if $prc & ph_include==1

Linear regression, absorbing indicators         Number of obs     =        198
                                                F(   2,     19)   =      28.74
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3809
                                                Adj R-squared     =     0.3071
                                                Root MSE          =     1.9271

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
ph_shipments |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |      2.625   .4215212     6.23   0.000     1.742746    3.507254
    post_trt |  -.3107143   .6811958    -0.46   0.653    -1.736473    1.115045
       _cons |   3.616162   .1658653    21.80   0.000     3.269001    3.963322
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg all_strips           post post_trt, cl(mkt_id) ab(mkt_id), if $prc & ph_include==1

Linear regression, absorbing indicators         Number of obs     =        198
                                                F(   2,     19)   =      10.96
                                                Prob > F          =     0.0007
                                                R-squared         =     0.2736
                                                Adj R-squared     =     0.1870
                                                Root MSE          =    18.7207

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
  all_strips |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   -.609375   4.221407    -0.14   0.887    -9.444881    8.226131
    post_trt |   19.15223   5.789853     3.31   0.004      7.03393    31.27053
       _cons |   37.75758   1.533782    24.62   0.000     34.54733    40.96782
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. 
. 
. 
. 
. 
. 
. *********************
. *********************
. **                 **
. **    APPENDIX     **
. **                 **
. *********************
. *********************
. 
. /*
> ***************************************************************
> ** APPENDIX FIGURE 1: THE DISTRIBUTION OF QUALITY COMPONENTS **
> ***************************************************************
> 
> use final, clear
> 
> ** POOLED API DENSITY
> twoway kdensity l_api_pct_cont, bwidth(0.8) scheme(s1color) lcolor(cranberry) xtitle(% of Correct Level) title(Active Ingredient Concentration) ytitle(Kernel
>  Density) xlabel(80[5]115) ylabel(0[0.05].2) lwidth(thick) nodraw, if tag_unitround==1 
> graph save kd_api.gph, replace
> 
> ** POOLED DISSOLUTION
> twoway kdensity l_dissol_min, bwidth(1) scheme(s1color) lcolor(cranberry) xtitle(Dissolution) ytitle(Kernel Density) title(Dissolution) nodraw xlabel(30[10]1
> 00) ylabel(0[0.05].15) lwidth(thick), if tag_unitround==1 
> graph save kd_dissol.gph, replace
> 
> ** POOLED UNIFORMITY 
> twoway kdensity l_uniform_absmax, bwidth(0.3) scheme(s1color) lcolor(cranberry) xtitle(Uniformity) ytitle(Kernel Density) title(Uniformity) nodraw xlabel(0[2
> ]12) /*ylabel(0[0.1].3)*/ lwidth(thick), if tag_unitround==1 
> graph save kd_uniform.gph, replace
> 
> graph combine kd_api.gph kd_dissol.gph kd_uniform.gph, cols(1) rows(3) title("") scheme(s1color) ysize(11) xsize(5.5)
> 
> XXX
> */
. 
. 
. /*
> ***********************************************************************************
> ** APPENDIX FIGURE 2: EXTREME HEAT AND HUMIDITY DURING DATA COLLECTION, BY ROUND **
> ***********************************************************************************
> 
> use hyd_weather, clear
> 
> rename qcptemp temp
> rename rhx humidity
> drop v5
> 
> tostring date, replace
> gen yea_s=substr(date,1,4)
> gen mon_s=substr(date,5,2)
> gen day_s=substr(date,7,2)
> 
> destring yea_s, gen(yea)
> destring mon_s, gen(mon)
> destring day_s, gen(day)
> 
> gen date2=mdy(mon,day,yea)
> format date2 %td
> 
> replace temp=. if temp>41
> replace humidity=. if humidity>100
> 
> bysort date: egen tempmax=max(temp)
> bysort date: egen humimax=max(humidity)
> bysort date: egen tempmean=mean(temp)
> bysort date: egen humimean=mean(humidity)
> 
> bysort date: keep if _n==1
> drop temp humidity yea_s mon_s day_s hrmn
> 
> 
> gen sample=0
> replace sample=1 if yea==2010 & mon==5 & day>=18
> replace sample=1 if yea==2010 & mon==6 & day<=19
> replace sample=1 if yea==2011 & mon==5 & day>=17
> replace sample=1 if yea==2011 & mon==6
> replace sample=1 if yea==2011 & mon==7 & day==1
> 
> 
> bysort yea: sum humimax tempmax if sample==1
> bysort yea: sum humimean tempmean if sample==1
> 
> gen dangerous = 0 
> replace dangerous = 1 if tempmax > 30 & humimax > 60
> 
> sum dangerous if sample==1 & yea==2010
> sum dangerous if sample==1 & yea==2011
> */
. 
. 
. ********************************************************************
. ** APPENDIX FIGURE 3: THE LOCATION OF SAMPLE MARKETS IN HYDERABAD ** 
. ********************************************************************
. 
. 
. /*
> ********************************************************************************
> ** APPENDIX FIGURE 4: QUALITY AND PRICE CHANGES FOR NON-NATIONAL DRUG SAMPLES **
> ********************************************************************************
> 
> use if tag_unitround==1 using final, clear
> 
> xtset unit round
> set scheme s1mono
> 
> xtgraph pass if medplus==0 & nn==1, gr(trt) label 
> graph save qual_nn.gph, replace
> 
> xtgraph pricepertab_usd if medplus==0 & nn==1, gr(trt) 
> graph save price_nn.gph, replace
> 
> graph combine qual_nn.gph price_nn.gph, cols(1) rows(2) title("") scheme(s1mono) ysize(11) xsize(7)
> 
> XXX
> */
. 
. 
. 
. 
. 
. 
. ********************************************************
. ** APPENDIX TABLE 1: THE IMPACT ON QUALITY COMPONENTS **
. ********************************************************
. 
. use final, clear

. 
. // PANEL A: ALL MANUFACTURERS
. areg l_api post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & dosage==500

Linear regression, absorbing indicators         Number of obs     =        753
                                                F(   2,     19)   =      13.81
                                                Prob > F          =     0.0002
                                                R-squared         =     0.0714
                                                Adj R-squared     =     0.0447
                                                Root MSE          =    16.6517

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
       l_api |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -6.859966   1.374065    -4.99   0.000    -9.735917   -3.984015
    post_trt |   3.528703   2.450926     1.44   0.166    -1.601144     8.65855
       _cons |   497.7004    .581031   856.58   0.000     496.4843    498.9165
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg l_api_pct post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1

Linear regression, absorbing indicators         Number of obs     =        756
                                                F(   2,     19)   =       0.44
                                                Prob > F          =     0.6498
                                                R-squared         =     0.0444
                                                Adj R-squared     =     0.0171
                                                Root MSE          =     2.5691

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
   l_api_pct |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0175918   .2848609     0.06   0.951     -.578629    .6138126
    post_trt |  -.2414774   .3717906    -0.65   0.524    -1.019644    .5366892
       _cons |    2.46576   .1041526    23.67   0.000     2.247766    2.683754
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ip_api_pass post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1

Linear regression, absorbing indicators         Number of obs     =        756
                                                F(   2,     19)   =       3.24
                                                Prob > F          =     0.0615
                                                R-squared         =     0.0725
                                                Adj R-squared     =     0.0459
                                                Root MSE          =     0.1714

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
 ip_api_pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0181832   .0156291    -1.16   0.259    -.0508953    .0145288
    post_trt |   .0416221   .0187446     2.22   0.039     .0023891    .0808551
       _cons |   .9703326   .0055444   175.01   0.000     .9587281    .9819372
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg l_dissol_min post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1

Linear regression, absorbing indicators         Number of obs     =        756
                                                F(   2,     19)   =       1.87
                                                Prob > F          =     0.1819
                                                R-squared         =     0.0403
                                                Adj R-squared     =     0.0128
                                                Root MSE          =     4.5684

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
l_dissol_min |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .1141399   .4979783     0.23   0.821    -.9281407    1.156421
    post_trt |   1.301955    .890326     1.46   0.160    -.5615188    3.165429
       _cons |    89.6179   .2099979   426.76   0.000     89.17837    90.05744
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ip_dissol_pass post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1

Linear regression, absorbing indicators         Number of obs     =        756
                                                F(   2,     19)   =       2.18
                                                Prob > F          =     0.1403
                                                R-squared         =     0.0525
                                                Adj R-squared     =     0.0254
                                                Root MSE          =     0.1465

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
ip_dissol_~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0239015   .0132542    -1.80   0.087    -.0516429    .0038398
    post_trt |   .0317145   .0151856     2.09   0.050    -.0000693    .0634984
       _cons |   .9842001   .0046319   212.48   0.000     .9745054    .9938948
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg l_uniform_absmax post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1

Linear regression, absorbing indicators         Number of obs     =        756
                                                F(   2,     19)   =      26.35
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0966
                                                Adj R-squared     =     0.0707
                                                Root MSE          =     1.0231

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
l_uniform_~x |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.4961555   .0807747    -6.14   0.000    -.6652189   -.3270921
    post_trt |   -.107284   .1756671    -0.61   0.549    -.4749595    .2603914
       _cons |    3.20858   .0381583    84.09   0.000     3.128714    3.288446
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ip_uniform_pass post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1

Linear regression, absorbing indicators         Number of obs     =        756
                                                F(   2,     19)   =       4.88
                                                Prob > F          =     0.0195
                                                R-squared         =     0.0322
                                                Adj R-squared     =     0.0045
                                                Root MSE          =     0.1022

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
ip_uniform~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0198684   .0092409    -2.15   0.045    -.0392099    -.000527
    post_trt |  -.0035705    .013874    -0.26   0.800    -.0326091    .0254681
       _cons |   1.000124   .0035786   279.48   0.000     .9926338    1.007614
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. // PANEL B: NON-NATIONAL MANUFACTURERS
. areg l_api post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & nn==1 & dosage==500

Linear regression, absorbing indicators         Number of obs     =        252
                                                F(   2,     19)   =       7.20
                                                Prob > F          =     0.0047
                                                R-squared         =     0.2119
                                                Adj R-squared     =     0.1400
                                                Root MSE          =    18.1015

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
       l_api |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -9.934148   3.048772    -3.26   0.004     -16.3153   -3.552994
    post_trt |   1.540418   5.286425     0.29   0.774    -9.524197    12.60503
       _cons |    493.778   1.334425   370.03   0.000      490.985     496.571
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg l_api_pct post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & nn==1

Linear regression, absorbing indicators         Number of obs     =        252
                                                F(   2,     19)   =       5.14
                                                Prob > F          =     0.0164
                                                R-squared         =     0.2178
                                                Adj R-squared     =     0.1464
                                                Root MSE          =     3.1041

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
   l_api_pct |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   1.815652   .5972855     3.04   0.007     .5655189    3.065785
    post_trt |  -2.521252   .9129654    -2.76   0.012    -4.432111   -.6103937
       _cons |   2.470937   .2470934    10.00   0.000     1.953765    2.988109
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ip_api_pass post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & nn==1

Linear regression, absorbing indicators         Number of obs     =        252
                                                F(   2,     19)   =       6.92
                                                Prob > F          =     0.0055
                                                R-squared         =     0.2281
                                                Adj R-squared     =     0.1576
                                                Root MSE          =     0.2176

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
 ip_api_pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1227048   .0358164    -3.43   0.003    -.1976694   -.0477402
    post_trt |   .1900615    .058585     3.24   0.004     .0674416    .3126814
       _cons |   .9730256    .015258    63.77   0.000     .9410901    1.004961
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg l_dissol_min post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & nn==1

Linear regression, absorbing indicators         Number of obs     =        252
                                                F(   2,     19)   =       2.84
                                                Prob > F          =     0.0830
                                                R-squared         =     0.1514
                                                Adj R-squared     =     0.0740
                                                Root MSE          =     6.0956

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
l_dissol_min |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.8847563   1.463679    -0.60   0.553    -3.948271    2.178758
    post_trt |   3.707327   1.907561     1.94   0.067    -.2852449    7.699899
       _cons |   88.90932   .5700759   155.96   0.000     87.71614    90.10251
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ip_dissol_pass post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & nn==1

Linear regression, absorbing indicators         Number of obs     =        252
                                                F(   2,     19)   =       3.03
                                                Prob > F          =     0.0722
                                                R-squared         =     0.1864
                                                Adj R-squared     =     0.1121
                                                Root MSE          =     0.1843

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
ip_dissol_~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0538028   .0331688    -1.62   0.121    -.1232258    .0156202
    post_trt |   .1077489   .0441591     2.44   0.025     .0153228    .2001749
       _cons |   .9703271   .0130143    74.56   0.000     .9430878    .9975664
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg l_uniform_absmax post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & nn==1

Linear regression, absorbing indicators         Number of obs     =        252
                                                F(   2,     19)   =      22.18
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1660
                                                Adj R-squared     =     0.0899
                                                Root MSE          =     1.0836

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
l_uniform_~x |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.6408985   .1723044    -3.72   0.001    -1.001536   -.2802612
    post_trt |  -.0332141   .2111367    -0.16   0.877    -.4751283    .4087002
       _cons |   3.370884   .0657677    51.25   0.000      3.23323    3.508537
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ip_uniform_pass post post_trt, cl(mktid) ab(mkt_id), if $urc & qq_sample==1 & nn==1

Linear regression, absorbing indicators         Number of obs     =        252
                                                F(   2,     19)   =       1.04
                                                Prob > F          =     0.3713
                                                R-squared         =     0.1727
                                                Adj R-squared     =     0.0972
                                                Root MSE          =     0.1033

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
ip_uniform~s |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0292651   .0310061    -0.94   0.357    -.0941617    .0356315
    post_trt |   .0037788   .0387771     0.10   0.923    -.0773827    .0849403
       _cons |   1.003113   .0119121    84.21   0.000     .9781809    1.028045
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. 
. 
. 
. ***************************************************
. ** APPENDIX TABLE 2: ADDITIONAL ROBUSTNESS TESTS **
. ***************************************************
. 
. use final, clear

. 
. 
. // PANEL A: DISTANCE TO CENTER
. areg pass post post_trt post_dfc,       cl(mktid) ab(mkt_id), if $urc & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(   3,     19)   =       4.73
                                                Prob > F          =     0.0125
                                                R-squared         =     0.0660
                                                Adj R-squared     =     0.0391
                                                Root MSE          =     0.2022

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0011964   .0208667     0.06   0.955    -.0424781    .0448709
    post_trt |   .0580608   .0186581     3.11   0.006     .0190089    .0971128
    post_dfc |  -.0045104   .0014598    -3.09   0.006    -.0075657   -.0014551
       _cons |   .9692032   .0056442   171.72   0.000     .9573898    .9810166
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt,                cl(mktid) ab(mkt_id), if $urc & common_dist==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        588
                                                F(   2,     14)   =       2.84
                                                Prob > F          =     0.0924
                                                R-squared         =     0.0863
                                                Adj R-squared     =     0.0607
                                                Root MSE          =     0.1957

                                 (Std. Err. adjusted for 15 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0560554   .0260939    -2.15   0.050    -.1120212   -.0000896
    post_trt |   .0568045    .026104     2.18   0.047     .0008169     .112792
       _cons |    .972465   .0070581   137.78   0.000      .957327    .9876031
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (15 categories)

. areg pass post post_trt post_dfc,       cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=. 

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   3,     19)   =       3.26
                                                Prob > F          =     0.0444
                                                R-squared         =     0.2545
                                                Adj R-squared     =     0.1859
                                                Root MSE          =     0.2345

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   -.103014   .0648602    -1.59   0.129     -.238768    .0327399
    post_trt |   .2277333   .0732163     3.11   0.006     .0744897    .3809768
    post_dfc |  -.0049422   .0038629    -1.28   0.216    -.0130274     .003143
       _cons |   .9685375   .0219705    44.08   0.000     .9225528    1.014522
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt,                cl(mktid) ab(mkt_id), if $urc & nn==1 & common_dist==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        185
                                                F(   2,     14)   =       2.97
                                                Prob > F          =     0.0843
                                                R-squared         =     0.2840
                                                Adj R-squared     =     0.2158
                                                Root MSE          =     0.2496

                                 (Std. Err. adjusted for 15 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1864781   .1023256    -1.82   0.090    -.4059447    .0329885
    post_trt |   .2453319   .1086099     2.26   0.040     .0123869     .478277
       _cons |   .9492791   .0290996    32.62   0.000     .8868667    1.011691
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (15 categories)

. areg ln_ppt post post_trt post_dfc,     cl(mktid) ab(mkt_id), if $urc & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(   3,     19)   =       8.04
                                                Prob > F          =     0.0012
                                                R-squared         =     0.1121
                                                Adj R-squared     =     0.0865
                                                Root MSE          =     0.2098

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0453482   .0329335    -1.38   0.185    -.1142788    .0235825
    post_trt |  -.0422255   .0270117    -1.56   0.135    -.0987616    .0143107
    post_dfc |   .0052759   .0013877     3.80   0.001     .0023714    .0081805
       _cons |  -1.850399   .0082022  -225.60   0.000    -1.867566   -1.833231
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt,              cl(mktid) ab(mkt_id), if $urc & common_dist==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        588
                                                F(   2,     14)   =       0.74
                                                Prob > F          =     0.4955
                                                R-squared         =     0.0937
                                                Adj R-squared     =     0.0683
                                                Root MSE          =     0.2028

                                 (Std. Err. adjusted for 15 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0171763   .0254501    -0.67   0.511    -.0717613    .0374087
    post_trt |  -.0019584   .0317192    -0.06   0.952    -.0699893    .0660725
       _cons |   -1.84131   .0081916  -224.78   0.000    -1.858879   -1.823741
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (15 categories)

. areg ln_ppt post post_trt post_dfc,     cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=. 

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   3,     19)   =       3.28
                                                Prob > F          =     0.0434
                                                R-squared         =     0.2009
                                                Adj R-squared     =     0.1274
                                                Root MSE          =     0.2046

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |    .088386   .0989647     0.89   0.383    -.1187495    .2955215
    post_trt |  -.0986299   .0509282    -1.94   0.068    -.2052238    .0079641
    post_dfc |  -.0036421   .0064349    -0.57   0.578    -.0171104    .0098263
       _cons |  -1.938359    .020626   -93.98   0.000     -1.98153   -1.895188
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt,              cl(mktid) ab(mkt_id), if $urc & nn==1 & common_dist==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        185
                                                F(   2,     14)   =       4.18
                                                Prob > F          =     0.0378
                                                R-squared         =     0.1946
                                                Adj R-squared     =     0.1179
                                                Root MSE          =     0.1710

                                 (Std. Err. adjusted for 15 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0122903   .0469074    -0.26   0.797    -.1128966     .088316
    post_trt |  -.0464924   .0511587    -0.91   0.379    -.1562168     .063232
       _cons |  -1.904466   .0136565  -139.46   0.000    -1.933757   -1.875176
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (15 categories)

. 
. 
. // PANEL B: TRAFFIC GROWTH
. areg pass post post_trt post_traff12,   cl(mktid) ab(mkt_id), if $urc & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(   3,     19)   =       1.75
                                                Prob > F          =     0.1901
                                                R-squared         =     0.0632
                                                Adj R-squared     =     0.0362
                                                Root MSE          =     0.2025

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0323495   .0196684    -1.64   0.116     -.073516    .0088169
    post_trt |   .0366178   .0186906     1.96   0.065    -.0025021    .0757378
post_traff12 |  -.0001457   .0001712    -0.85   0.405     -.000504    .0002127
       _cons |   .9691684   .0061943   156.46   0.000     .9562036    .9821332
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt,                cl(mktid) ab(mkt_id), if $urc & common_traf==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        550
                                                F(   2,     13)   =       1.97
                                                Prob > F          =     0.1791
                                                R-squared         =     0.0799
                                                Adj R-squared     =     0.0541
                                                Root MSE          =     0.2028

                                 (Std. Err. adjusted for 14 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0488943   .0287863    -1.70   0.113    -.1110833    .0132947
    post_trt |   .0496434   .0287956     1.72   0.108    -.0125656    .1118524
       _cons |   .9668033   .0073297   131.90   0.000     .9509685    .9826382
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (14 categories)

. areg pass post post_trt post_traff12,   cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   3,     19)   =       2.94
                                                Prob > F          =     0.0595
                                                R-squared         =     0.2519
                                                Adj R-squared     =     0.1831
                                                Root MSE          =     0.2349

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1646763   .0777086    -2.12   0.047    -.3273222   -.0020303
    post_trt |   .2191118   .0765268     2.86   0.010     .0589393    .3792842
post_traff12 |   .0001663   .0005343     0.31   0.759    -.0009519    .0012845
       _cons |    .971724   .0226165    42.97   0.000      .924387    1.019061
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt,                cl(mktid) ab(mkt_id), if $urc & nn==1 & common_traf==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        187
                                                F(   2,     13)   =       3.17
                                                Prob > F          =     0.0755
                                                R-squared         =     0.2720
                                                Adj R-squared     =     0.2082
                                                Root MSE          =     0.2424

                                 (Std. Err. adjusted for 14 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1939409   .1004327    -1.93   0.076    -.4109125    .0230308
    post_trt |   .2527947   .1068174     2.37   0.034     .0220297    .4835596
       _cons |   .9530161   .0262759    36.27   0.000     .8962504    1.009782
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (14 categories)

. areg ln_ppt post post_trt post_traff12, cl(mktid) ab(mkt_id), if $urc & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        787
                                                F(   3,     19)   =       3.97
                                                Prob > F          =     0.0237
                                                R-squared         =     0.1147
                                                Adj R-squared     =     0.0892
                                                Root MSE          =     0.2095

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0490331    .020197     2.43   0.025     .0067603    .0913058
    post_trt |  -.0519749   .0310867    -1.67   0.111    -.1170401    .0130903
post_traff12 |  -.0006703   .0002143    -3.13   0.006    -.0011187   -.0002218
       _cons |  -1.850463   .0077444  -238.94   0.000    -1.866672   -1.834254
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt,              cl(mktid) ab(mkt_id), if $urc & common_traf==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        550
                                                F(   2,     13)   =       1.57
                                                Prob > F          =     0.2456
                                                R-squared         =     0.1123
                                                Adj R-squared     =     0.0873
                                                Root MSE          =     0.1908

                                 (Std. Err. adjusted for 14 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0443285   .0304584     1.46   0.169    -.0214728    .1101299
    post_trt |  -.0634633   .0358884    -1.77   0.100    -.1409954    .0140689
       _cons |   -1.85253   .0090989  -203.60   0.000    -1.872187   -1.832873
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (14 categories)

. areg ln_ppt post post_trt post_traff12, cl(mktid) ab(mkt_id), if $urc & nn==1  & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   3,     19)   =       3.27
                                                Prob > F          =     0.0437
                                                R-squared         =     0.1988
                                                Adj R-squared     =     0.1251
                                                Root MSE          =     0.2049

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |    .053534   .0497571     1.08   0.295    -.0506089    .1576768
    post_trt |  -.1114905   .0514917    -2.17   0.043    -.2192639   -.0037171
post_traff12 |  -.0000311   .0005111    -0.06   0.952    -.0011008    .0010386
       _cons |  -1.936187   .0183455  -105.54   0.000    -1.974585    -1.89779
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt,              cl(mktid) ab(mkt_id), if $urc & nn==1 & common_traf==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        187
                                                F(   2,     13)   =       4.15
                                                Prob > F          =     0.0403
                                                R-squared         =     0.2474
                                                Adj R-squared     =     0.1814
                                                Root MSE          =     0.1677

                                 (Std. Err. adjusted for 14 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0025547   .0470901     0.05   0.958    -.0991772    .1042866
    post_trt |  -.0613374    .051319    -1.20   0.253    -.1722054    .0495306
       _cons |  -1.900045   .0126239  -150.51   0.000    -1.927317   -1.872773
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (14 categories)

. 
. 
. 
. 
. 
. 
. ****************************************************************
. ** APPENDIX TABLE 3: ESTIMATES FOR 18 CANDIDATE ENTRY MARKETS **
. ****************************************************************
. 
. use final, clear

. areg pass post post_trt,                        cl(mktid) ab(mkt_id), if $urc & drpm==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        707
                                                F(   2,     17)   =       2.59
                                                Prob > F          =     0.1040
                                                R-squared         =     0.0681
                                                Adj R-squared     =     0.0423
                                                Root MSE          =     0.2036

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0450293   .0221928    -2.03   0.058    -.0918521    .0017934
    post_trt |   .0457784   .0222046     2.06   0.055    -.0010693     .092626
       _cons |   .9685404   .0068759   140.86   0.000     .9540335    .9830472
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. areg pass post post_trt $demo_m $hlth_m,        cl(mktid) ab(mkt_id), if $urc & drpm==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        707
                                                F(  11,     17)   =       9.41
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0800
                                                Adj R-squared     =     0.0420
                                                Root MSE          =     0.2036

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0652241   .0168078    -3.88   0.001    -.1006855   -.0297626
    post_trt |   .0511803   .0240501     2.13   0.048      .000439    .1019217
       inc_m |   3.84e-07   3.22e-06     0.12   0.906    -6.41e-06    7.17e-06
       edu_m |   .0163275   .0100879     1.62   0.124    -.0049561    .0376111
       deg_m |  -.2290745   .0877404    -2.61   0.018    -.4141905   -.0439585
       cst_m |  -.1254871   .1396654    -0.90   0.381    -.4201554    .1691812
       tfw_m |  -.0405088   .0739718    -0.55   0.591    -.1965756    .1155581
      fevr_m |  -.1166856   .6271182    -0.19   0.855    -1.439789    1.206418
      diar_m |   2.380206   .7218027     3.30   0.004     .8573351    3.903077
      cold_m |  -.3007506   .3107191    -0.97   0.347    -.9563105    .3548093
      injy_m |  -2.553692   .6633225    -3.85   0.001    -3.953181   -1.154204
       _cons |   1.076549   .3642389     2.96   0.009      .308072    1.845026
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. areg pass post post_trt,                        cl(mktid) ab(mkt_id), if $urc & nn==1 & drpm==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        228
                                                F(   2,     17)   =       3.99
                                                Prob > F          =     0.0380
                                                R-squared         =     0.2627
                                                Adj R-squared     =     0.1953
                                                Root MSE          =     0.2424

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1695397   .0734683    -2.31   0.034    -.3245443   -.0145352
    post_trt |   .2283935   .0818713     2.79   0.013     .0556601     .401127
       _cons |   .9642047   .0249342    38.67   0.000      .911598    1.016811
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. areg pass post post_trt $demo_m $hlth_m,        cl(mktid) ab(mkt_id), if $urc & nn==1 & drpm==1 & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        228
                                                F(  11,     17)   =      15.62
                                                Prob > F          =     0.0000
                                                R-squared         =     0.3677
                                                Adj R-squared     =     0.2787
                                                Root MSE          =     0.2295

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1517576   .0787541    -1.93   0.071    -.3179142     .014399
    post_trt |   .1425866   .0822279     1.73   0.101    -.0308991    .3160724
       inc_m |  -.0000306   .0000112    -2.73   0.014    -.0000542   -6.92e-06
       edu_m |   .1110803   .0330148     3.36   0.004     .0414252    .1807354
       deg_m |  -1.355149   .3642838    -3.72   0.002    -2.123721   -.5865774
       cst_m |  -.5511831   .4170575    -1.32   0.204    -1.431097    .3287312
       tfw_m |  -.5105326   .2096822    -2.43   0.026    -.9529235   -.0681418
      fevr_m |  -2.497973   1.855978    -1.35   0.196    -6.413744    1.417797
      diar_m |   .1544282   1.770092     0.09   0.931     -3.58014    3.888997
      cold_m |  -.4611376   1.001315    -0.46   0.651    -2.573727    1.651452
      injy_m |  -12.23386   2.509396    -4.88   0.000    -17.52822   -6.939493
       _cons |   2.725467   .9539798     2.86   0.011     .7127452    4.738188
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. areg ln_ppt post post_trt,                  cl(mktid) ab(mkt_id), if $urc & drpm==1

Linear regression, absorbing indicators         Number of obs     =        707
                                                F(   2,     17)   =       0.52
                                                Prob > F          =     0.6049
                                                R-squared         =     0.1057
                                                Adj R-squared     =     0.0809
                                                Root MSE          =     0.2107

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0010375    .026175    -0.04   0.969     -.056262     .054187
    post_trt |  -.0180973   .0322365    -0.56   0.582    -.0861103    .0499158
       _cons |  -1.855045    .008901  -208.41   0.000    -1.873825   -1.836265
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. areg ln_ppt post post_trt $demo_m $hlth_m,  cl(mktid) ab(mkt_id), if $urc & drpm==1

Linear regression, absorbing indicators         Number of obs     =        707
                                                F(  11,     17)   =       2.24
                                                Prob > F          =     0.0657
                                                R-squared         =     0.1141
                                                Adj R-squared     =     0.0775
                                                Root MSE          =     0.2111

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0075289   .0344856     0.22   0.830    -.0652293     .080287
    post_trt |  -.0384023   .0464372    -0.83   0.420    -.1363762    .0595715
       inc_m |   2.83e-07   9.18e-06     0.03   0.976    -.0000191    .0000197
       edu_m |  -.0250211   .0238855    -1.05   0.310     -.075415    .0253728
       deg_m |  -.0215034   .1751953    -0.12   0.904    -.3911332    .3481264
       cst_m |   .0414495   .2246839     0.18   0.856    -.4325921     .515491
       tfw_m |  -.1060889   .0809343    -1.31   0.207    -.2768454    .0646676
      fevr_m |  -.1076423   .9520754    -0.11   0.911    -2.116346    1.901061
      diar_m |  -1.949532   1.089147    -1.79   0.091    -4.247431    .3483667
      cold_m |  -.2725503   .6471393    -0.42   0.679    -1.637895    1.092794
      injy_m |  -.2963202   1.453911    -0.20   0.841    -3.363805    2.771164
       _cons |  -1.156787   .4448981    -2.60   0.019     -2.09544   -.2181336
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. areg ln_ppt post post_trt,                  cl(mktid) ab(mkt_id), if $urc & nn==1 & drpm==1

Linear regression, absorbing indicators         Number of obs     =        228
                                                F(   2,     17)   =       4.73
                                                Prob > F          =     0.0233
                                                R-squared         =     0.1772
                                                Adj R-squared     =     0.1021
                                                Root MSE          =     0.2062

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0606948   .0594373     1.02   0.321     -.064707    .1860967
    post_trt |  -.1194775    .062796    -1.90   0.074    -.2519655    .0130105
       _cons |  -1.947316   .0197194   -98.75   0.000     -1.98892   -1.905711
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. areg ln_ppt post post_trt $demo_m $hlth_m,  cl(mktid) ab(mkt_id), if $urc & nn==1 & drpm==1

Linear regression, absorbing indicators         Number of obs     =        228
                                                F(  11,     17)   =       5.73
                                                Prob > F          =     0.0007
                                                R-squared         =     0.2513
                                                Adj R-squared     =     0.1460
                                                Root MSE          =     0.2011

                                 (Std. Err. adjusted for 18 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .1787237   .0659437     2.71   0.015     .0395947    .3178528
    post_trt |  -.2873641   .0747821    -3.84   0.001    -.4451406   -.1295875
       inc_m |  -.0000427   .0000161    -2.65   0.017    -.0000767   -8.68e-06
       edu_m |   .0524651   .0398394     1.32   0.205    -.0315886    .1365188
       deg_m |  -.5788967   .2738942    -2.11   0.050    -1.156763   -.0010305
       cst_m |  -1.422572   .5138324    -2.77   0.013    -2.506663   -.3384801
       tfw_m |  -.4049077   .1276239    -3.17   0.006    -.6741707   -.1356448
      fevr_m |  -2.064867   1.747533    -1.18   0.254    -5.751839    1.622106
      diar_m |  -8.651681   2.147057    -4.03   0.001    -13.18158   -4.121786
      cold_m |  -1.235688   1.202314    -1.03   0.318    -3.772349    1.300973
      injy_m |  -3.682105    2.34873    -1.57   0.135    -8.637492    1.273282
       _cons |  -.0382662   .7874312    -0.05   0.962    -1.699601    1.623068
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (18 categories)

. 
. 
. *************************************************
. ** PROPORTIONAL SELECTION FOR APPENDIX TABLE 3 **
. *************************************************
. 
. use if $urc & drpm==1 & ln_ppt~=.         using final, clear

. psroutine pass "$demo_m $hlth_m" 

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       1.52821
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.04578                   0.068
Controlled   |        0.05118                   0.080
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.086
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. use if $urc & drpm==1 & ln_ppt~=. & nn==1 using final, clear

. psroutine pass "$demo_m $hlth_m" 

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       1.05684
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.22839                   0.263
Controlled   |        0.14259                   0.368
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.398
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. use if $urc & drpm==1 & ln_ppt~=.         using final, clear

. psroutine ln_ppt "$demo_m $hlth_m" 

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       2.27245
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01810                   0.106
Controlled   |       -0.03840                   0.114
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.132
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. use if $urc & drpm==1 & ln_ppt~=. & nn==1 using final, clear

. psroutine ln_ppt "$demo_m $hlth_m" 

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       3.59470
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.11948                   0.177
Controlled   |       -0.28736                   0.251
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.290
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. 
. 
. 
. 
. *********************************************************
. ** APPENDIX TABLE 4: ROBUSTNESS TO UNOBSERVABLE TRENDS ** 
. *********************************************************
. 
. use final, clear

. 
. areg pass post post_trt $demo_bix, cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=.
note: inc_mb omitted because of collinearity
note: edu_mb omitted because of collinearity
note: deg_mb omitted because of collinearity
note: cst_mb omitted because of collinearity
note: tfw_mb omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   7,     19)   =       1.79
                                                Prob > F          =     0.1488
                                                R-squared         =     0.2709
                                                Adj R-squared     =     0.1902
                                                Root MSE          =     0.2338

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -1.977598   1.424016    -1.39   0.181    -4.958096    1.002901
    post_trt |   .2296564   .0824221     2.79   0.012      .057145    .4021678
      inc_mb |          0  (omitted)
      edu_mb |          0  (omitted)
      deg_mb |          0  (omitted)
      cst_mb |          0  (omitted)
      tfw_mb |          0  (omitted)
 post_inc_mb |   8.17e-06   .0000131     0.62   0.541    -.0000193    .0000356
 post_edu_mb |  -.1063427   .0990169    -1.07   0.296    -.3135874     .100902
 post_deg_mb |   1.087638   1.111075     0.98   0.340     -1.23787    3.413145
 post_cst_mb |  -1.147367    .947545    -1.21   0.241    -3.130601    .8358676
 post_tfw_mb |   .7822405   .5587737     1.40   0.178    -.3872862    1.951767
       _cons |   .9661304    .019218    50.27   0.000     .9259067    1.006354
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt $hlth_bix, cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=.
note: fevr_mb omitted because of collinearity
note: diar_mb omitted because of collinearity
note: cold_mb omitted because of collinearity
note: injy_mb omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   6,     19)   =       3.12
                                                Prob > F          =     0.0268
                                                R-squared         =     0.2772
                                                Adj R-squared     =     0.2007
                                                Root MSE          =     0.2323

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0053822   .2901198    -0.02   0.985    -.6126098    .6018454
    post_trt |   .1474021   .0455079     3.24   0.004      .052153    .2426513
     fevr_mb |          0  (omitted)
     diar_mb |          0  (omitted)
     cold_mb |          0  (omitted)
     injy_mb |          0  (omitted)
post_fevr_mb |  -.9509309   3.055097    -0.31   0.759    -7.345323    5.443461
post_diar_mb |  -2.668894   17.42823    -0.15   0.880    -39.14659    33.80881
post_cold_mb |  -1.637008   1.474222    -1.11   0.281    -4.722591    1.448575
post_injy_mb |   3.440796   3.116904     1.10   0.283    -3.082959    9.964551
       _cons |   .9730776   .0247294    39.35   0.000     .9213184    1.024837
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt $supp_bix missd post_missd, cl(mktid) ab(mkt_id), if $urc & nn==1 & ln_ppt~=.
note: post_missd omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(  10,     19)   =          .
                                                Prob > F          =          .
                                                R-squared         =     0.3445
                                                Adj R-squared     =     0.2594
                                                Root MSE          =     0.2231

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.8056018    .331523    -2.43   0.025    -1.499487   -.1117161
    post_trt |   .1424915   .0414136     3.44   0.003      .055812    .2291711
     retpctb |  -.0000492   .0013529    -0.04   0.971    -.0028809    .0027825
      signsb |   .0062193    .012259     0.51   0.618     -.019439    .0318777
      phageb |   .0088076   .0046294     1.90   0.072    -.0008818     .018497
     tr_allb |  -.0031396   .0010448    -3.01   0.007    -.0053263   -.0009529
post_retpctb |    .008298   .0036919     2.25   0.037     .0005707    .0160252
 post_signsb |   .0029041   .0240524     0.12   0.905    -.0474382    .0532465
 post_phageb |  -.0133348   .0087809    -1.52   0.145    -.0317135     .005044
post_tr_allb |   .0015158   .0016742     0.91   0.377    -.0019884      .00502
       missd |  -.1062693   .0712642    -1.49   0.152     -.255427    .0428884
  post_missd |          0  (omitted)
       _cons |   1.100624   .1330282     8.27   0.000     .8221924    1.379055
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg pass post post_trt pass_phb post_pass_phb, cl(mktid) ab(mkt_id), if $urc & nn==1  & ln_ppt~=.

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   4,     19)   =       8.87
                                                Prob > F          =     0.0003
                                                R-squared         =     0.2741
                                                Adj R-squared     =     0.2039
                                                Root MSE          =     0.2318

                                  (Std. Err. adjusted for 20 clusters in mktid)
-------------------------------------------------------------------------------
              |               Robust
         pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
         post |   .5775321   .1864368     3.10   0.006     .1873153    .9677489
     post_trt |   .1900665   .0679285     2.80   0.011     .0478906    .3322424
     pass_phb |   .4125694   .2428416     1.70   0.106    -.0957039    .9208426
post_pass_phb |   -.755314   .2175527    -3.47   0.003    -1.210657   -.2999709
        _cons |    .577193   .2331377     2.48   0.023     .0892302    1.065156
--------------+----------------------------------------------------------------
       mkt_id |   absorbed                                      (20 categories)

. 
. areg ln_ppt post post_trt $demo_bix, cl(mktid) ab(mkt_id), if $urc & nn==1
note: inc_mb omitted because of collinearity
note: edu_mb omitted because of collinearity
note: deg_mb omitted because of collinearity
note: cst_mb omitted because of collinearity
note: tfw_mb omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   7,     19)   =       2.64
                                                Prob > F          =     0.0438
                                                R-squared         =     0.2172
                                                Adj R-squared     =     0.1305
                                                Root MSE          =     0.2042

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   -1.87172   1.141837    -1.64   0.118    -4.261612    .5181716
    post_trt |  -.1893227   .0820164    -2.31   0.032    -.3609851   -.0176604
      inc_mb |          0  (omitted)
      edu_mb |          0  (omitted)
      deg_mb |          0  (omitted)
      cst_mb |          0  (omitted)
      tfw_mb |          0  (omitted)
 post_inc_mb |   8.54e-06   .0000112     0.77   0.453    -.0000148    .0000319
 post_edu_mb |  -.0671737   .0611771    -1.10   0.286    -.1952189    .0608715
 post_deg_mb |   1.066495   .6985106     1.53   0.143    -.3955042    2.528495
 post_cst_mb |   .1761449   .9505732     0.19   0.855    -1.813428    2.165717
 post_tfw_mb |   .6377344   .3498695     1.82   0.084    -.0945509     1.37002
       _cons |  -1.941455   .0179891  -107.92   0.000    -1.979107   -1.903803
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt $hlth_bix, cl(mktid) ab(mkt_id), if $urc & nn==1
note: fevr_mb omitted because of collinearity
note: diar_mb omitted because of collinearity
note: cold_mb omitted because of collinearity
note: injy_mb omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   6,     19)   =       2.62
                                                Prob > F          =     0.0504
                                                R-squared         =     0.2124
                                                Adj R-squared     =     0.1290
                                                Root MSE          =     0.2044

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .2289611   .1746603     1.31   0.206    -.1366072    .5945294
    post_trt |  -.1128711   .0520146    -2.17   0.043     -.221739   -.0040032
     fevr_mb |          0  (omitted)
     diar_mb |          0  (omitted)
     cold_mb |          0  (omitted)
     injy_mb |          0  (omitted)
post_fevr_mb |   .2541395   1.690022     0.15   0.882    -3.283117    3.791396
post_diar_mb |  -11.66966   14.87999    -0.78   0.443    -42.81384    19.47452
post_cold_mb |  -2.307628   1.037151    -2.22   0.038     -4.47841   -.1368466
post_injy_mb |   -3.08327   4.001496    -0.77   0.450     -11.4585    5.291959
       _cons |  -1.936751   .0190826  -101.49   0.000    -1.976692   -1.896811
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt $supp_bix missd post_missd, cl(mktid) ab(mkt_id), if $urc & nn==1
note: post_missd omitted because of collinearity

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(  10,     19)   =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2799
                                                Adj R-squared     =     0.1864
                                                Root MSE          =     0.1972

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.7498393   .3421738    -2.19   0.041    -1.466017   -.0336614
    post_trt |  -.0810261    .070658    -1.15   0.266     -.228915    .0668627
     retpctb |   -.000386    .002813    -0.14   0.892    -.0062737    .0055017
      signsb |  -.0369131   .0228014    -1.62   0.122    -.0846369    .0108108
      phageb |   .0005864   .0053954     0.11   0.915    -.0107063    .0118791
     tr_allb |    -.00274   .0011124    -2.46   0.023    -.0050683   -.0004117
post_retpctb |   .0068052   .0039614     1.72   0.102     -.001486    .0150965
 post_signsb |   .0057528   .0177337     0.32   0.749    -.0313642    .0428698
 post_phageb |   .0024831   .0055481     0.45   0.660    -.0091292    .0140954
post_tr_allb |   .0026256   .0013527     1.94   0.067    -.0002055    .0054568
       missd |  -.0541587   .0531244    -1.02   0.321    -.1653494    .0570321
  post_missd |          0  (omitted)
       _cons |   -1.62402   .2624174    -6.19   0.000    -2.173266   -1.074774
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg ln_ppt post post_trt ppt_phb post_ppt_phb, cl(mktid) ab(mkt_id), if $urc & nn==1

Linear regression, absorbing indicators         Number of obs     =        262
                                                F(   4,     19)   =      27.51
                                                Prob > F          =     0.0000
                                                R-squared         =     0.4241
                                                Adj R-squared     =     0.3684
                                                Root MSE          =     0.1741

                                 (Std. Err. adjusted for 20 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .5347618   .1628821     3.28   0.004     .1938457    .8756778
    post_trt |  -.0719158   .0430406    -1.67   0.111     -.162001    .0181693
     ppt_phb |   6.351576   .7396111     8.59   0.000     4.803552      7.8996
post_ppt_phb |  -3.352098   1.013556    -3.31   0.004    -5.473494   -1.230701
       _cons |  -2.908276   .1174628   -24.76   0.000    -3.154129   -2.662424
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. 
. *************************************************
. ** PROPORTIONAL SELECTION FOR APPENDIX TABLE 4 **
. *************************************************
. 
. use if $urc & nn==1 & ln_ppt~=. using final, clear

. psroutine pass "$demo_bix"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.36915
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.21207                   0.252
Controlled   |        0.22966                   0.271
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.382
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine pass "$hlth_bix"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.49848
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.21207                   0.252
Controlled   |        0.14740                   0.277
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.382
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine pass "$supp_bix" "$supp_bix missd post_missd"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       1.57927
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.21207                   0.252
Controlled   |        0.14682                   0.344
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.428
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine pass "pass_phb post_pass_phb" "pass_phb post_pass_phb"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       1.31437
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.21207                   0.252
Controlled   |        0.19007                   0.274
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.413
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine ln_ppt "$demo_bix"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       5.45097
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.11017                   0.199
Controlled   |       -0.18932                   0.217
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.306
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine ln_ppt "$hlth_bix"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.70702
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.11017                   0.199
Controlled   |       -0.11287                   0.212
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.306
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine ln_ppt "$supp_bix" "$supp_bix missd post_missd"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       1.26578
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.11017                   0.199
Controlled   |       -0.07882                   0.280
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.379
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. psroutine ln_ppt "ppt_phb post_ppt_phb" "ppt_phb post_ppt_phb"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       6.19514
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.11017                   0.199
Controlled   |       -0.07192                   0.424
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.488
Beta         |    0.000000
Unr. Controls|   post mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. 
. 
. ****************************************************************************
. ** APPENDIX TABLE 5: A TEST FOR TREATMENT SPILLOVERS INTO CONTROL MARKETS **
. ****************************************************************************
. 
. use final, clear

. 
. areg pass post post_trt,                 cl(mktid) ab(mkt_id), if $urc & csamp==1 & disttrt>=5

Linear regression, absorbing indicators         Number of obs     =        629
                                                F(   2,     15)   =       1.97
                                                Prob > F          =     0.1740
                                                R-squared         =     0.0609
                                                Adj R-squared     =     0.0348
                                                Root MSE          =     0.2161

                                 (Std. Err. adjusted for 16 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0439373   .0259098    -1.70   0.111    -.0991628    .0112882
    post_trt |   .0446864     .02592     1.72   0.105    -.0105608    .0999336
       _cons |   .9614649   .0073751   130.37   0.000     .9457452    .9771845
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (16 categories)

. areg pass post post_trt,                 cl(mktid) ab(mkt_id), if $urc & csamp==1 & disttrt>=10

Linear regression, absorbing indicators         Number of obs     =        471
                                                F(   2,     11)   =       2.48
                                                Prob > F          =     0.1292
                                                R-squared         =     0.0531
                                                Adj R-squared     =     0.0261
                                                Root MSE          =     0.1843

                                 (Std. Err. adjusted for 12 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.0705923   .0356547    -1.98   0.073    -.1490677    .0078832
    post_trt |   .0713413   .0356623     2.00   0.071    -.0071508    .1498335
       _cons |   .9786749   .0075731   129.23   0.000     .9620067     .995343
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (12 categories)

. areg pass post post_trt,                 cl(mktid) ab(mkt_id), if $urc & csamp==1 & nn==1 & disttrt>=5

Linear regression, absorbing indicators         Number of obs     =        204
                                                F(   2,     15)   =       3.69
                                                Prob > F          =     0.0498
                                                R-squared         =     0.2588
                                                Adj R-squared     =     0.1910
                                                Root MSE          =     0.2424

                                 (Std. Err. adjusted for 16 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.1852091   .0850317    -2.18   0.046    -.3664498   -.0039683
    post_trt |   .2440629   .0924416     2.64   0.019     .0470283    .4410974
       _cons |   .9654946   .0275035    35.10   0.000     .9068723    1.024117
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (16 categories)

. areg pass post post_trt,                 cl(mktid) ab(mkt_id), if $urc & csamp==1 & nn==1 & disttrt>=10

Linear regression, absorbing indicators         Number of obs     =        153
                                                F(   2,     11)   =       2.67
                                                Prob > F          =     0.1131
                                                R-squared         =     0.2709
                                                Adj R-squared     =     0.2027
                                                Root MSE          =     0.2214

                                 (Std. Err. adjusted for 12 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
        pass |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |  -.2401743   .1440681    -1.67   0.124     -.557266    .0769174
    post_trt |   .2990281   .1486714     2.01   0.069    -.0281954    .6262516
       _cons |   .9687482   .0329753    29.38   0.000       .89617    1.041326
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (12 categories)

. 
. areg ln_ppt post post_trt,               cl(mktid) ab(mkt_id), if $urc & csamp==1 & disttrt>=5

Linear regression, absorbing indicators         Number of obs     =        629
                                                F(   2,     15)   =       1.06
                                                Prob > F          =     0.3702
                                                R-squared         =     0.1263
                                                Adj R-squared     =     0.1020
                                                Root MSE          =     0.2045

                                 (Std. Err. adjusted for 16 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0323732    .030887     1.05   0.311    -.0334609    .0982073
    post_trt |  -.0515079   .0362042    -1.42   0.175    -.1286754    .0256596
       _cons |   -1.85317   .0097176  -190.70   0.000    -1.873882   -1.832457
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (16 categories)

. areg ln_ppt post post_trt,               cl(mktid) ab(mkt_id), if $urc & csamp==1 & disttrt>=10

Linear regression, absorbing indicators         Number of obs     =        471
                                                F(   2,     11)   =       1.22
                                                Prob > F          =     0.3332
                                                R-squared         =     0.1366
                                                Adj R-squared     =     0.1121
                                                Root MSE          =     0.2054

                                 (Std. Err. adjusted for 12 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .0452481   .0378359     1.20   0.257    -.0380282    .1285244
    post_trt |  -.0643828   .0423866    -1.52   0.157    -.1576752    .0289095
       _cons |  -1.862214   .0097913  -190.19   0.000    -1.883765   -1.840664
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (12 categories)

. areg ln_ppt post post_trt,               cl(mktid) ab(mkt_id), if $urc & csamp==1 & nn==1 & disttrt>=5

Linear regression, absorbing indicators         Number of obs     =        204
                                                F(   2,     15)   =       5.41
                                                Prob > F          =     0.0170
                                                R-squared         =     0.2424
                                                Adj R-squared     =     0.1732
                                                Root MSE          =     0.2069

                                 (Std. Err. adjusted for 16 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .1057408   .0673974     1.57   0.138    -.0379133    .2493949
    post_trt |  -.1645235   .0703989    -2.34   0.034    -.3145752   -.0144718
       _cons |   -1.94086   .0213131   -91.06   0.000    -1.986288   -1.895433
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (16 categories)

. areg ln_ppt post post_trt,               cl(mktid) ab(mkt_id), if $urc & csamp==1 & nn==1 & disttrt>=10

Linear regression, absorbing indicators         Number of obs     =        153
                                                F(   2,     11)   =       5.78
                                                Prob > F          =     0.0192
                                                R-squared         =     0.2087
                                                Adj R-squared     =     0.1346
                                                Root MSE          =     0.2211

                                 (Std. Err. adjusted for 12 clusters in mktid)
------------------------------------------------------------------------------
             |               Robust
      ln_ppt |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
        post |   .1705714   .0923571     1.85   0.092    -.0327053    .3738481
    post_trt |  -.2293541   .0946238    -2.42   0.034    -.4376197   -.0210885
       _cons |  -1.954716   .0208596   -93.71   0.000    -2.000627   -1.908804
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (12 categories)

. 
. 
. 
. 
. 
. 
. 
. *************************************************************************
. ** APPENDIX TABLE 6: THE IMPACT OF CHAIN ENTRY ON CONSUMER PREFERENCES **
. *************************************************************************
. 
. use consumer, clear

. 
. areg qs r2 r3 r2_trt r3_trt [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2
(sum of wgt is   1.0489e+06)

Linear regression, absorbing indicators         Number of obs     =      2,575
                                                F(   4,     19)   =       5.79
                                                Prob > F          =     0.0032
                                                R-squared         =     0.0781
                                                Adj R-squared     =     0.0697
                                                Root MSE          =     0.2699

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
          qs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |    .003239   .0460112     0.07   0.945    -.0930635    .0995415
          r3 |  -.0347312    .033839    -1.03   0.318    -.1055571    .0360947
      r2_trt |   .2756691   .0771543     3.57   0.002     .1141833    .4371549
      r3_trt |   .2787713   .0741036     3.76   0.001     .1236706    .4338721
       _cons |   .8407762   .0276432    30.42   0.000     .7829184    .8986341
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg qs r2 r3 r2_trt r3_trt $demo_m $hlth_m [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2
(sum of wgt is   1.0489e+06)

Linear regression, absorbing indicators         Number of obs     =      2,575
                                                F(  13,     19)   =      20.96
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1031
                                                Adj R-squared     =     0.0918
                                                Root MSE          =     0.2667

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
          qs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.0373049   .0370477    -1.01   0.327    -.1148467    .0402369
          r3 |   .0296846   .1563615     0.19   0.851    -.2975837     .356953
      r2_trt |   .2775212   .0788571     3.52   0.002     .1124713    .4425711
      r3_trt |    .302912   .0726439     4.17   0.001     .1508667    .4549574
       inc_m |   .0028627   .0037291     0.77   0.452    -.0049423    .0106678
       edu_m |  -.0170168   .0194482    -0.87   0.393    -.0577224    .0236888
       deg_m |  -.1340679   .1662131    -0.81   0.430     -.481956    .2138202
       cst_m |   .0081577   .1474713     0.06   0.956    -.3005033    .3168187
       tfw_m |   .1402887    .091979     1.53   0.144    -.0522255     .332803
      fevr_m |    1.21568   .6325034     1.92   0.070    -.1081651    2.539525
      diar_m |  -3.485511   3.234563    -1.08   0.295    -10.25553    3.284507
      cold_m |  -1.344434   .6943098    -1.94   0.068    -2.797641    .1087736
      injy_m |    2.70492    1.30671     2.07   0.052    -.0300554    5.439895
       _cons |   .5536212   .4269331     1.30   0.210      -.33996    1.447202
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. areg cs r2 r3 r2_trt r3_trt [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2
(sum of wgt is   1.0688e+06)

Linear regression, absorbing indicators         Number of obs     =      2,632
                                                F(   4,     19)   =      11.71
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0931
                                                Adj R-squared     =     0.0851
                                                Root MSE          =     0.2307

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
          cs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.1387053   .0336414    -4.12   0.001    -.2091176   -.0682931
          r3 |  -.0237201   .0085188    -2.78   0.012    -.0415501   -.0058901
      r2_trt |   .0449636   .0629483     0.71   0.484    -.0867888     .176716
      r3_trt |  -.0025193   .0140663    -0.18   0.860    -.0319603    .0269217
       _cons |   .9983221   .0111707    89.37   0.000     .9749414    1.021703
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg cs r2 r3 r2_trt r3_trt $demo_m $hlth_m [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2
(sum of wgt is   1.0688e+06)

Linear regression, absorbing indicators         Number of obs     =      2,632
                                                F(  13,     19)   =      15.36
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1187
                                                Adj R-squared     =     0.1079
                                                Root MSE          =     0.2279

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
          cs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.1470205   .0429613    -3.42   0.003    -.2369395   -.0571015
          r3 |  -.2034571     .15784    -1.29   0.213    -.5338201    .1269059
      r2_trt |   .0763854   .0531836     1.44   0.167    -.0349293       .1877
      r3_trt |    .019432   .0503799     0.39   0.704    -.0860143    .1248784
       inc_m |  -.0109759   .0048669    -2.26   0.036    -.0211624   -.0007894
       edu_m |  -.0129041   .0137752    -0.94   0.361    -.0417359    .0159278
       deg_m |   .2187591   .2216871     0.99   0.336    -.2452374    .6827556
       cst_m |  -.1329757   .1626612    -0.82   0.424    -.4734295     .207478
       tfw_m |  -.0276262    .138722    -0.20   0.844    -.3179746    .2627223
      fevr_m |   -.258786   .8920035    -0.29   0.775    -2.125771    1.608199
      diar_m |     4.1684   2.747522     1.52   0.146    -1.582229    9.919029
      cold_m |  -1.865886   .9469928    -1.97   0.064    -3.847965    .1161924
      injy_m |   -2.75301   1.733697    -1.59   0.129    -6.381679     .875659
       _cons |   1.490591   .5745373     2.59   0.018     .2880702    2.693111
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. areg fs r2 r3 r2_trt r3_trt [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2
(sum of wgt is   1.0684e+06)

Linear regression, absorbing indicators         Number of obs     =      2,631
                                                F(   4,     19)   =     212.93
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2349
                                                Adj R-squared     =     0.2282
                                                Root MSE          =     0.4370

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
          fs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.6469799   .0531014   -12.18   0.000    -.7581224   -.5358374
          r3 |  -.3721374   .0184823   -20.13   0.000    -.4108214   -.3334534
      r2_trt |  -.0138824   .0689678    -0.20   0.843    -.1582337    .1304689
      r3_trt |  -.1317524   .0468885    -2.81   0.011    -.2298911   -.0336136
       _cons |   .9980649   .0179941    55.47   0.000     .9604028    1.035727
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. areg fs r2 r3 r2_trt r3_trt $demo_m $hlth_m [pw=tr_all], cl(mkt_id) ab(mkt_id), if int_loc==2
(sum of wgt is   1.0684e+06)

Linear regression, absorbing indicators         Number of obs     =      2,631
                                                F(  13,     19)   =      82.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.2403
                                                Adj R-squared     =     0.2310
                                                Root MSE          =     0.4362

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
          fs |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.6911904   .0614436   -11.25   0.000    -.8197933   -.5625875
          r3 |  -.2657149   .2385917    -1.11   0.279    -.7650931    .2336633
      r2_trt |    .006548     .09243     0.07   0.944    -.1869102    .2000062
      r3_trt |  -.1144626   .0713106    -1.61   0.125    -.2637175    .0347923
       inc_m |   .0006452   .0089337     0.07   0.943    -.0180532    .0193436
       edu_m |  -.0264718    .027605    -0.96   0.350    -.0842497    .0313062
       deg_m |  -.3079353   .2702673    -1.14   0.269    -.8736112    .2577407
       cst_m |   .1594489   .2531196     0.63   0.536    -.3703365    .6892343
       tfw_m |  -.3468389   .1798986    -1.93   0.069     -.723371    .0296932
      fevr_m |  -1.065283   1.284936    -0.83   0.417    -3.754685    1.624119
      diar_m |   .9679668   4.283644     0.23   0.824    -7.997804    9.933737
      cold_m |  -.2587568   1.268417    -0.20   0.841    -2.913585    2.396071
      injy_m |   1.276602    2.11871     0.60   0.554    -3.157909    5.711113
       _cons |   2.610171   .6631403     3.94   0.001     1.222202     3.99814
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. 
. *************************************************
. ** PROPORTIONAL SELECTION FOR APPENDIX TABLE 6 **
. *************************************************
. 
. use if int_loc==2 using consumer, clear

. psroutine_3round qs "$demo_m $hlth_m" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       1.39856
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.27567                   0.078
Controlled   |        0.27752                   0.103
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.141
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       2.08352
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.27877                   0.078
Controlled   |        0.30291                   0.103
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.141
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. psroutine_3round cs "$demo_m $hlth_m" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -3.09872
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |        0.04496                   0.093
Controlled   |        0.07639                   0.119
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.166
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -0.45544
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.00252                   0.093
Controlled   |        0.01943                   0.119
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.166
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. psroutine_3round fs "$demo_m $hlth_m" "[pw=tr_all]"

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |      -0.05092
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.01388                   0.235
Controlled   |        0.00655                   0.240
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.267
Beta         |    0.000000
Unr. Controls|   r3_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

                 ---- Bound Estimate ----
-------------+----------------------------------------------------------------
delta        |       0.43400
-------------+----------------------------------------------------------------

                 ---- Inputs from Regressions ----
             |      Coeff.                      R-Squared
-------------+----------------------------------------------------------------
Uncontrolled |       -0.13175                   0.235
Controlled   |       -0.11446                   0.240
-------------+----------------------------------------------------------------

                 ---- Other Inputs ----
-------------+----------------------------------------------------------------
R_max        |   0.267
Beta         |    0.000000
Unr. Controls|   r2_trt r2 r3 mk_*
-------------+----------------------------------------------------------------

. 
. 
. 
. *************************************************************************
. ** APPENDIX TABLE 7: CHAIN ENTRY AND INCUMBENT SHOPPER CHARACTERISTICS **
. *************************************************************************
. 
. use consumer, clear

. 
. foreach var in ln_incr_usd edu_years hsize scst tfw {
  2. areg `var' r2 r3 r2_trt r3_trt, cl(mkt_id) ab(mkt_id), if int_loc==1 & mp==0 & dm_sample
  3. }

Linear regression, absorbing indicators         Number of obs     =      2,224
                                                F(   4,     19)   =       6.34
                                                Prob > F          =     0.0020
                                                R-squared         =     0.0551
                                                Adj R-squared     =     0.0452
                                                Root MSE          =     0.5759

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
 ln_incr_usd |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |   .1216281   .0559094     2.18   0.042     .0046085    .2386477
          r3 |  -.0287296   .0532786    -0.54   0.596     -.140243    .0827839
      r2_trt |  -.0445534   .1662249    -0.27   0.792     -.392466    .3033593
      r3_trt |    .008543   .1394113     0.06   0.952    -.2832482    .3003341
       _cons |   5.241625   .0479455   109.32   0.000     5.141274    5.341976
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

Linear regression, absorbing indicators         Number of obs     =      2,224
                                                F(   4,     19)   =      14.90
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0557
                                                Adj R-squared     =     0.0459
                                                Root MSE          =     4.9022

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
   edu_years |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |   .2740863   .3911915     0.70   0.492    -.5446869     1.09286
          r3 |  -1.498536   .4796699    -3.12   0.006    -2.502497   -.4945759
      r2_trt |  -.4060921   .9961705    -0.41   0.688    -2.491101    1.678917
      r3_trt |   -.032737   .8622879    -0.04   0.970    -1.837526    1.772052
       _cons |   11.52342   .3171406    36.34   0.000     10.85963     12.1872
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

Linear regression, absorbing indicators         Number of obs     =      2,224
                                                F(   4,     19)   =      51.37
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0685
                                                Adj R-squared     =     0.0588
                                                Root MSE          =     1.5631

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
       hsize |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |   .2739341   .1419332     1.93   0.069    -.0231355    .5710037
          r3 |   .8637681   .0655509    13.18   0.000     .7265685    1.000968
      r2_trt |   .1816136    .229915     0.79   0.439     -.299604    .6628312
      r3_trt |  -.2723689   .1720277    -1.58   0.130    -.6324271    .0876893
       _cons |   3.948056    .057037    69.22   0.000     3.828677    4.067436
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

Linear regression, absorbing indicators         Number of obs     =      2,224
                                                F(   4,     19)   =       1.29
                                                Prob > F          =     0.3092
                                                R-squared         =     0.0264
                                                Adj R-squared     =     0.0162
                                                Root MSE          =     0.3596

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
        scst |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.0352239   .0399876    -0.88   0.389    -.1189189    .0484712
          r3 |   .0161233   .0451405     0.36   0.725    -.0783568    .1106034
      r2_trt |  -.0101768   .0791916    -0.13   0.899    -.1759267    .1555732
      r3_trt |   -.027365   .0558263    -0.49   0.630    -.1442108    .0894809
       _cons |   .1685322   .0256143     6.58   0.000     .1149209    .2221435
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

Linear regression, absorbing indicators         Number of obs     =      2,224
                                                F(   4,     19)   =       5.00
                                                Prob > F          =     0.0063
                                                R-squared         =     0.0375
                                                Adj R-squared     =     0.0275
                                                Root MSE          =     0.4918

                                (Std. Err. adjusted for 20 clusters in mkt_id)
------------------------------------------------------------------------------
             |               Robust
         tfw |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
          r2 |  -.0612871   .0873087    -0.70   0.491    -.2440264    .1214522
          r3 |  -.1450292   .0540892    -2.68   0.015    -.2582392   -.0318193
      r2_trt |   .0328922   .1315361     0.25   0.805     -.242416    .3082004
      r3_trt |   .0839941   .0969812     0.87   0.397    -.1189898    .2869781
       _cons |   .6054974   .0432936    13.99   0.000     .5148828     .696112
-------------+----------------------------------------------------------------
      mkt_id |   absorbed                                      (20 categories)

. 
. 
. 
. log close
      name:  <unnamed>
       log:  /Users/danielbennett/Dropbox/Hyderabad (DB & WY)/Analysis/replication/replication.log
  log type:  text
 closed on:  15 Feb 2018, 10:10:39
---------------------------------------------------------------------------------------------------------------------------------------------------------------
